From 4df8ebe245e9e2ebad022246823d1104a00d56d9 Mon Sep 17 00:00:00 2001
From: Winni <winnus@posteo.de>
Date: Mon, 19 Jun 2023 09:12:31 +0200
Subject: [PATCH] v0.01
Multiple island models, operator stacks, first operators, evolutionary phases, python process pool paralelization, experiments and first simple fitness function tested.
---
.gitignore | 26 +
diagramms/Evolution_02.drawio | 492 +++++++++
diagramms/Evolution_03.drawio | 493 +++++++++
diagramms/Evolutionary_phase_00.drawio | 121 +++
src/PyGMA.ipynb | 1360 +++++++++++++++++++++++-
src/config.py | 105 ++
src/controller.py | 254 +++++
src/evolutionary_phase.py | 61 ++
src/experiment.py | 92 ++
src/genetic_operators.py | 135 +++
src/individual.py | 7 +
src/population.py | 118 ++
src/population_old.py | 33 +
src/testing_notebook.ipynb | 363 +++++++
14 files changed, 3659 insertions(+), 1 deletion(-)
create mode 100755 diagramms/Evolution_02.drawio
create mode 100755 diagramms/Evolution_03.drawio
create mode 100755 diagramms/Evolutionary_phase_00.drawio
create mode 100755 src/config.py
create mode 100755 src/controller.py
create mode 100755 src/evolutionary_phase.py
create mode 100755 src/experiment.py
create mode 100755 src/genetic_operators.py
create mode 100755 src/individual.py
create mode 100755 src/population.py
create mode 100755 src/population_old.py
create mode 100755 src/testing_notebook.ipynb
diff --git a/.gitignore b/.gitignore
index 9dff1a6..c12ea59 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,30 @@
# no ipython checkpoints
*.ipynb_checkpoints
+# IPython
+profile_default/
+ipython_config.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+
+# no drawio backups
+*.bkp
+
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
diff --git a/diagramms/Evolution_02.drawio b/diagramms/Evolution_02.drawio
new file mode 100755
index 0000000..b8ec2ea
--- /dev/null
+++ b/diagramms/Evolution_02.drawio
@@ -0,0 +1,492 @@
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+ <mxCell id="6sdkXRkWTDobi1n3qOID-32" value="On all populations set the epoch counder one up<br>so again you would pack this here into functions.<br>Like _populatios.mutat()<br>_populations_instantiate()<br>_populations_apply_operatos()" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;strokeColor=none;fillColor=none;" parent="1" vertex="1">
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+ </mxCell>
+ <mxCell id="2305VUciKoEtOX_F4iJQ-4" value="+ apply_operator(gene_0, gene_1)" style="text;strokeColor=none;fillColor=none;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;whiteSpace=wrap;html=1;" parent="2305VUciKoEtOX_F4iJQ-1" vertex="1">
+ <mxGeometry y="60" width="230" height="26" as="geometry" />
+ </mxCell>
+ <mxCell id="2305VUciKoEtOX_F4iJQ-6" value="Thies will be an interface to the operators used to produce offsprings<br>" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;strokeColor=none;fillColor=none;" parent="1" vertex="1">
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+ <mxCell id="ju8TFpcfmoSFrrAgH8_M-2" value="+ name: string" style="text;strokeColor=none;fillColor=none;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;whiteSpace=wrap;html=1;" parent="ju8TFpcfmoSFrrAgH8_M-1" vertex="1">
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+ <mxGeometry y="52" width="230" height="8" as="geometry" />
+ </mxCell>
+ <mxCell id="ju8TFpcfmoSFrrAgH8_M-5" value="+ apply_modification(gene_0)" style="text;strokeColor=none;fillColor=none;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;whiteSpace=wrap;html=1;" parent="ju8TFpcfmoSFrrAgH8_M-1" vertex="1">
+ <mxGeometry y="60" width="230" height="26" as="geometry" />
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+ <mxCell id="ju8TFpcfmoSFrrAgH8_M-6" value="Unlike an operator here it is clear that no new offrprings will be generated<br>Here an inplace modification takes place like extending the gene string&nbsp;<br>or do something else with it" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;strokeColor=none;fillColor=none;" parent="1" vertex="1">
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+ <mxCell id="HAsKsfTJxQwT5OBsK-yM-1" value="population.mutate()" style="whiteSpace=wrap;html=1;rounded=1;" vertex="1" parent="1">
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+ <mxCell id="HAsKsfTJxQwT5OBsK-yM-2" value="call mutate on all the populations" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;strokeColor=none;fillColor=none;" vertex="1" parent="1">
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+ <mxCell id="HAsKsfTJxQwT5OBsK-yM-4" value="population.instantiiate_<br>individuals(mpi=true)" style="whiteSpace=wrap;html=1;rounded=1;" vertex="1" parent="1">
+ <mxGeometry x="3438" y="1129" width="120" height="60" as="geometry" />
+ </mxCell>
+ <mxCell id="HAsKsfTJxQwT5OBsK-yM-5" value="On all populations set the epoch counder one up<br>so again you would pack this here into functions.<br>Like _populatios.mutat()<br>_populations_instantiate()<br>_populations_apply_operatos()" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;strokeColor=none;fillColor=none;" vertex="1" parent="1">
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diff --git a/diagramms/Evolution_03.drawio b/diagramms/Evolution_03.drawio
new file mode 100755
index 0000000..da58e8f
--- /dev/null
+++ b/diagramms/Evolution_03.drawio
@@ -0,0 +1,493 @@
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+ </mxCell>
+ <mxCell id="6sdkXRkWTDobi1n3qOID-31" value="controller.pops_stagnation_check()<br>config.stagnation_test_true?<br>Have stagnation operators (like a heavy mutation)<br>Or revombination operators that merge two populations?<br>Stop this behaviour automatically if stagnation resolved (how to check? with epoch and diversity tracking))<br>or does this makes sense as one could restart the algo right away?<br>Think it makes sense as stucking in a local optima can maybe be changed by having high exploration (mutation)" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;strokeColor=none;fillColor=none;" parent="1" vertex="1">
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+ <mxCell id="2305VUciKoEtOX_F4iJQ-2" value="+ name: string" style="text;strokeColor=none;fillColor=none;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;whiteSpace=wrap;html=1;" parent="2305VUciKoEtOX_F4iJQ-1" vertex="1">
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+ <mxCell id="2305VUciKoEtOX_F4iJQ-3" value="" style="line;strokeWidth=1;fillColor=none;align=left;verticalAlign=middle;spacingTop=-1;spacingLeft=3;spacingRight=3;rotatable=0;labelPosition=right;points=[];portConstraint=eastwest;strokeColor=inherit;" parent="2305VUciKoEtOX_F4iJQ-1" vertex="1">
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+ <mxCell id="2305VUciKoEtOX_F4iJQ-4" value="+ apply_operator(gene_0, gene_1)" style="text;strokeColor=none;fillColor=none;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;whiteSpace=wrap;html=1;" parent="2305VUciKoEtOX_F4iJQ-1" vertex="1">
+ <mxGeometry y="60" width="230" height="26" as="geometry" />
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+ <mxCell id="ju8TFpcfmoSFrrAgH8_M-5" value="+ apply_modification(gene_0)" style="text;strokeColor=none;fillColor=none;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;whiteSpace=wrap;html=1;" parent="ju8TFpcfmoSFrrAgH8_M-1" vertex="1">
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+ <mxCell id="ju8TFpcfmoSFrrAgH8_M-6" value="Unlike an operator here it is clear that no new offrprings will be generated<br>Here an inplace modification takes place like extending the gene string&nbsp;<br>or do something else with it" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;strokeColor=none;fillColor=none;" parent="1" vertex="1">
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+ </mxCell>
+ <mxCell id="HAsKsfTJxQwT5OBsK-yM-2" value="call mutate on all the populations" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;strokeColor=none;fillColor=none;" parent="1" vertex="1">
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+ <mxGeometry x="3438" y="1287" width="120" height="60" as="geometry" />
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+ <mxCell id="HAsKsfTJxQwT5OBsK-yM-5" value="On all populations set the epoch counder one up<br>so again you would pack this here into functions.<br>Like _populatios.mutat()<br>_populations_instantiate()<br>_populations_apply_operatos()" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;strokeColor=none;fillColor=none;" parent="1" vertex="1">
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+ <mxCell id="TjDierjk1-rNDTtvZhPD-1" value="Idea, define evolutional phases.<br>controller has phase array<br>loops and asks if phase.phase_completed(population_fitness, epochs): init_new_phase(phase_shift_operators)<br>mpiw knows from (gene, phase) which experiment he shall coduct<br>Each phase has its own:<br>Opearator stack (no! this is population specific!) it can create new populations or modifie the ones given when it starts (an thus can change the operators)<br>start_one_time_operator_stack<br>and abbility to call a population fusion/modification setup<br>experiment<br><br><br>Now it is definend on which fitness or value the cotnroller shall switch into the new phase (phase.phase_copmleted()<br>by doing that he will:<br>&nbsp;change its operator stack<br>change the experiment<br>apply phase operators (that can extend the genes or remove some)<br>Recombine new populations<br style="border-color: var(--border-color);">build entire new populations<br style="border-color: var(--border-color);">sort out some populations.<br>stuff like this.&nbsp;<br>Then it will swicht to the new phase." style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;strokeColor=none;fillColor=none;" parent="1" vertex="1">
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+ <mxGeometry relative="1" as="geometry" />
+ </mxCell>
+ <mxCell id="TjDierjk1-rNDTtvZhPD-2" value="for all phases" style="strokeWidth=2;html=1;shape=mxgraph.flowchart.loop_limit;whiteSpace=wrap;" parent="1" vertex="1">
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diff --git a/diagramms/Evolutionary_phase_00.drawio b/diagramms/Evolutionary_phase_00.drawio
new file mode 100755
index 0000000..d3c76a9
--- /dev/null
+++ b/diagramms/Evolutionary_phase_00.drawio
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diff --git a/src/PyGMA.ipynb b/src/PyGMA.ipynb
index 78d7a30..a15835f 100755
--- a/src/PyGMA.ipynb
+++ b/src/PyGMA.ipynb
@@ -2,9 +2,1367 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "1c9a80e2-37b8-46f5-94ba-d547329f2234",
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Init controller\n",
+ "[<population.Population object at 0x7f1e2039f650>, <population.Population object at 0x7f1e203f9b90>, <population.Population object at 0x7f1e203fa350>]\n",
+ "[<genetic_operators.Removal_operator object at 0x7f1e4a7542d0>, <genetic_operators.Single_point_crossover_operator object at 0x7f1e4a7747d0>]\n",
+ "[ True False False False False True False False True True True False\n",
+ " False False False True False True False True False True True True\n",
+ " True True False True False True True False False True True True\n",
+ " True False True True False True True False False True False True\n",
+ " False False False True False False True True False True True True]\n",
+ "Evolutionary phase Phase_0\n",
+ "epoch 0, Pop max fitness [9.925775054145103e-07, 2.12027020723521e-06, 1.6586828731041255e-06]\n",
+ "epoch 1, Pop max fitness [1.3549254249046132e-06, 1.545657167057718e-06, 1.9955061202172707e-06]\n",
+ "epoch 2, Pop max fitness [1.3549254249046132e-06, 1.585273443816324e-06, 0.0005668934240362812]\n",
+ "epoch 3, Pop max fitness [2.2883504654504845e-06, 3.542180282807674e-06, 3.122268015486449e-05]\n",
+ "epoch 4, Pop max fitness [2.2883504654504845e-06, 4.140598150808866e-06, 1.0268627289903886e-05]\n",
+ "epoch 5, Pop max fitness [2.2883504654504845e-06, 3.443455025033918e-06, 4.2322668020992044e-05]\n",
+ "epoch 6, Pop max fitness [2.2883504654504845e-06, 3.4950614781314002e-06, 0.0005347593582887701]\n",
+ "epoch 7, Pop max fitness [2.320476347384591e-06, 4.202051441513747e-06, 0.0005347593582887701]\n",
+ "epoch 8, Pop max fitness [2.841118946285805e-06, 1.0342972984154565e-05, 2.7753878604534985e-05]\n",
+ "epoch 9, Pop max fitness [2.841118946285805e-06, 2.738000711880185e-05, 9.838646202282565e-05]\n",
+ "epoch 10, Pop max fitness [2.841118946285805e-06, 1.7602224921230043e-05, 0.0007262164124909223]\n",
+ "epoch 11, Pop max fitness [2.841118946285805e-06, 0.00011133377866844801, 0.0078125]\n",
+ "epoch 12, Pop max fitness [2.841118946285805e-06, 1.8345930872532473e-05, 0.0005875440658049354]\n",
+ "epoch 13, Pop max fitness [2.841118946285805e-06, 1.8117254873541563e-05, 0.00023529411764705883]\n",
+ "epoch 14, Pop max fitness [3.054386404315237e-06, 6.878052135635188e-05, 6.046680372475511e-05]\n",
+ "epoch 15, Pop max fitness [3.054386404315237e-06, 9.405568096313017e-05, 6.383657835939994e-05]\n",
+ "epoch 16, Pop max fitness [3.054386404315237e-06, 0.00010121457489878542, 8.246062505153789e-05]\n",
+ "epoch 17, Pop max fitness [3.054386404315237e-06, 2.684275514038761e-05, 0.00018642803877703205]\n",
+ "epoch 18, Pop max fitness [3.054386404315237e-06, 0.0003891050583657588, 0.00012208521548040532]\n",
+ "epoch 19, Pop max fitness [3.054386404315237e-06, 7.651109410864575e-05, 0.00012682308180088776]\n",
+ "epoch 20, Pop max fitness [3.054386404315237e-06, 9.793360101850946e-05, 0.011235955056179775]\n",
+ "epoch 21, Pop max fitness [3.054386404315237e-06, 0.0010660980810234541, 0.00012729124236252545]\n",
+ "epoch 22, Pop max fitness [3.054386404315237e-06, 0.001597444089456869, 5.140595280933532e-05]\n",
+ "epoch 23, Pop max fitness [3.054386404315237e-06, 0.015625, 0.00023952095808383233]\n",
+ "epoch 24, Pop max fitness [3.054386404315237e-06, 4.11370274383973e-05, 0.00022026431718061675]\n",
+ "epoch 25, Pop max fitness [3.054386404315237e-06, 7.530120481927712e-05, 0.00034698126301179735]\n",
+ "epoch 26, Pop max fitness [3.054386404315237e-06, 7.551159102922299e-05, 6.390593047034765e-05]\n",
+ "epoch 27, Pop max fitness [3.054386404315237e-06, 3.292831505811848e-05, 6.503219093451258e-05]\n",
+ "epoch 28, Pop max fitness [3.054386404315237e-06, 5.421817393190197e-05, 3.566969859104691e-05]\n",
+ "epoch 29, Pop max fitness [3.054386404315237e-06, 8.86839304717985e-05, 0.0002969121140142518]\n",
+ "epoch 30, Pop max fitness [3.054386404315237e-06, 4.1981528127623844e-05, 0.0007102272727272727]\n",
+ "epoch 31, Pop max fitness [3.054386404315237e-06, 2.4898538455792647e-05, 9.588647041902388e-05]\n",
+ "epoch 32, Pop max fitness [3.054386404315237e-06, 3.6621987841500034e-05, 0.00022401433691756272]\n",
+ "epoch 33, Pop max fitness [3.054386404315237e-06, 1.4829094683769556e-05, 0.00015328019619865113]\n",
+ "epoch 34, Pop max fitness [3.054386404315237e-06, 1.4829974344144385e-05, 0.0002069108214359611]\n",
+ "epoch 35, Pop max fitness [3.054386404315237e-06, 2.9383245673317074e-05, 0.0015015015015015015]\n",
+ "epoch 36, Pop max fitness [3.054386404315237e-06, 3.2156408772268315e-05, 0.0002018978396931153]\n",
+ "epoch 37, Pop max fitness [3.054386404315237e-06, 6.165988407941794e-05, 0.0011389521640091116]\n",
+ "epoch 38, Pop max fitness [3.054386404315237e-06, 5.3239631581749454e-05, 0.002207505518763797]\n",
+ "epoch 39, Pop max fitness [3.054386404315237e-06, 0.00011065619121389841, 0.0015220700152207]\n",
+ "epoch 40, Pop max fitness [3.054386404315237e-06, 4.957120904178853e-05, 0.0035460992907801418]\n",
+ "epoch 41, Pop max fitness [3.054386404315237e-06, 0.0001771479185119575, 0.00017406440382941688]\n",
+ "epoch 42, Pop max fitness [3.054386404315237e-06, 0.0002595380223202699, 6.509992839007877e-05]\n",
+ "epoch 43, Pop max fitness [3.054386404315237e-06, 0.002793296089385475, 0.0025]\n",
+ "epoch 44, Pop max fitness [3.054386404315237e-06, 7.204610951008646e-05, 9.007386056566384e-05]\n",
+ "epoch 45, Pop max fitness [3.054386404315237e-06, 2.8506271379703534e-05, 2.896703551358554e-05]\n",
+ "epoch 46, Pop max fitness [3.054386404315237e-06, 0.00012610340479192938, 3.5258444397433186e-05]\n",
+ "epoch 47, Pop max fitness [3.054386404315237e-06, 5.7766737912310095e-05, 3.5124692658939236e-05]\n",
+ "epoch 48, Pop max fitness [3.054386404315237e-06, 0.00022558087074216106, 5.733944954128441e-05]\n",
+ "epoch 49, Pop max fitness [3.054386404315237e-06, 0.00018162005085361425, 5.126627704296114e-05]\n",
+ "epoch 50, Pop max fitness [3.054386404315237e-06, 0.0001444460494005489, 8.568246080027418e-05]\n",
+ "epoch 51, Pop max fitness [3.054386404315237e-06, 0.0007369196757553427, 0.0004508566275924256]\n",
+ "epoch 52, Pop max fitness [3.054386404315237e-06, 0.0016181229773462784, 8.340979230961715e-05]\n",
+ "epoch 53, Pop max fitness [3.054386404315237e-06, 5.709717939933767e-05, 5.5154155865644475e-05]\n",
+ "epoch 54, Pop max fitness [3.054386404315237e-06, 6.145148405333988e-05, 3.765769158350593e-05]\n",
+ "epoch 55, Pop max fitness [3.054386404315237e-06, 2.1634251346732147e-05, 9.709680551509856e-05]\n",
+ "epoch 56, Pop max fitness [3.054386404315237e-06, 7.544892108042855e-05, 5.426819341184132e-05]\n",
+ "epoch 57, Pop max fitness [3.054386404315237e-06, 8.532423208191126e-05, 4.411505205576143e-05]\n",
+ "epoch 58, Pop max fitness [3.054386404315237e-06, 8.628127696289905e-05, 3.7085110328203226e-05]\n",
+ "epoch 59, Pop max fitness [3.054386404315237e-06, 8.771929824561403e-05, 0.0001545833977430824]\n",
+ "epoch 60, Pop max fitness [3.054386404315237e-06, 9.74184120798831e-05, 3.963064241271351e-05]\n",
+ "epoch 61, Pop max fitness [3.054386404315237e-06, 0.00020234722784297855, 4.732831653178096e-05]\n",
+ "epoch 62, Pop max fitness [3.054386404315237e-06, 0.00010932546190007652, 0.00010136847440446021]\n",
+ "epoch 63, Pop max fitness [3.054386404315237e-06, 0.00023741690408357076, 0.0001002004008016032]\n",
+ "epoch 64, Pop max fitness [3.054386404315237e-06, 0.00019201228878648233, 3.435717721432007e-05]\n",
+ "epoch 65, Pop max fitness [3.054386404315237e-06, 0.0005449591280653951, 4.0064102564102564e-05]\n",
+ "epoch 66, Pop max fitness [3.054386404315237e-06, 0.000576036866359447, 4.369101712687871e-05]\n",
+ "epoch 67, Pop max fitness [3.054386404315237e-06, 0.0003246753246753247, 4.416570974295557e-05]\n",
+ "epoch 68, Pop max fitness [3.054386404315237e-06, 0.0008090614886731392, 3.009419482981733e-05]\n",
+ "epoch 69, Pop max fitness [3.054386404315237e-06, 0.00015477480266212662, 4.02592696968477e-05]\n",
+ "epoch 70, Pop max fitness [3.054386404315237e-06, 0.00010147133434804668, 0.00011189437171310283]\n",
+ "epoch 71, Pop max fitness [3.054386404315237e-06, 0.0005117707267144319, 0.00011129660545353366]\n",
+ "epoch 72, Pop max fitness [3.054386404315237e-06, 0.0006261740763932373, 0.0001514004542013626]\n",
+ "epoch 73, Pop max fitness [3.054386404315237e-06, 0.00042211903756859433, 6.01286753652817e-05]\n",
+ "epoch 74, Pop max fitness [3.054386404315237e-06, 6.44039415212211e-05, 0.0001661405549094534]\n",
+ "epoch 75, Pop max fitness [3.054386404315237e-06, 3.28709486555782e-05, 0.00030674846625766873]\n",
+ "epoch 76, Pop max fitness [3.054386404315237e-06, 3.402865212508933e-05, 0.0017985611510791368]\n",
+ "epoch 77, Pop max fitness [3.054386404315237e-06, 5.2656521510189036e-05, 0.0006724949562878278]\n",
+ "epoch 78, Pop max fitness [3.054386404315237e-06, 2.9463759575721862e-05, 0.02702702702702703]\n",
+ "epoch 79, Pop max fitness [3.054386404315237e-06, 1.963286541670757e-05, 0.0009216589861751152]\n",
+ "epoch 80, Pop max fitness [3.054386404315237e-06, 1.8372221201543268e-05, 0.0021929824561403508]\n",
+ "epoch 81, Pop max fitness [3.054386404315237e-06, 1.8422652493506014e-05, 0.005780346820809248]\n",
+ "epoch 82, Pop max fitness [3.054386404315237e-06, 0.0008149959250203749, 0.005128205128205128]\n",
+ "epoch 83, Pop max fitness [3.054386404315237e-06, 0.000333000333000333, 0.00012283503255128362]\n",
+ "epoch 84, Pop max fitness [3.054386404315237e-06, 2.399232245681382e-05, 0.00026845637583892615]\n",
+ "epoch 85, Pop max fitness [3.054386404315237e-06, 7.984031936127745e-05, 0.008849557522123894]\n",
+ "epoch 86, Pop max fitness [3.054386404315237e-06, 6.868131868131868e-05, 0.0007288629737609329]\n",
+ "epoch 87, Pop max fitness [3.054386404315237e-06, 0.0007112375533428165, 0.0001820498816675769]\n",
+ "epoch 88, Pop max fitness [3.054386404315237e-06, 4.699689820471849e-05, 0.0001199760047990402]\n",
+ "epoch 89, Pop max fitness [3.054386404315237e-06, 1.8361426315596197e-05, 0.0001010611419909045]\n",
+ "epoch 90, Pop max fitness [3.054386404315237e-06, 8.724480893386844e-05, 0.00013008976193573565]\n",
+ "epoch 91, Pop max fitness [3.054386404315237e-06, 8.01153661272232e-05, 0.00020496003279360525]\n",
+ "epoch 92, Pop max fitness [3.054386404315237e-06, 6.368209896198179e-05, 0.0008802816901408451]\n",
+ "epoch 93, Pop max fitness [3.054386404315237e-06, 4.345181194055792e-05, 0.00031725888324873094]\n",
+ "epoch 94, Pop max fitness [3.054386404315237e-06, 6.72314105149926e-05, 0.00015842839036755386]\n",
+ "epoch 95, Pop max fitness [3.054386404315237e-06, 8.594018563080097e-05, 0.00031776294884016526]\n",
+ "epoch 96, Pop max fitness [3.054386404315237e-06, 0.000471253534401508, 0.00012330456226880394]\n",
+ "epoch 97, Pop max fitness [3.054386404315237e-06, 0.00010109179134654265, 0.00016954899966090201]\n",
+ "epoch 98, Pop max fitness [3.054386404315237e-06, 0.0012804097311139564, 0.00018221574344023323]\n",
+ "epoch 99, Pop max fitness [3.054386404315237e-06, 0.00032690421706440013, 0.00010206164523372117]\n",
+ "epoch 100, Pop max fitness [3.054386404315237e-06, 0.00015229972586049346, 0.0006203473945409429]\n",
+ "epoch 101, Pop max fitness [3.054386404315237e-06, 5.547542438699656e-05, 0.0005241090146750524]\n",
+ "epoch 102, Pop max fitness [3.054386404315237e-06, 0.007407407407407408, 0.0013227513227513227]\n",
+ "epoch 103, Pop max fitness [3.054386404315237e-06, 2.5145213608589605e-05, 0.00013377926421404682]\n",
+ "epoch 104, Pop max fitness [3.054386404315237e-06, 2.4183796856106408e-05, 0.00012344155042587335]\n",
+ "epoch 105, Pop max fitness [3.054386404315237e-06, 0.0001874062968515742, 0.0006402048655569782]\n",
+ "epoch 106, Pop max fitness [3.054386404315237e-06, 1.360655291588429e-05, 0.002824858757062147]\n",
+ "epoch 107, Pop max fitness [3.054386404315237e-06, 5.4344872561273845e-05, 0.0001123721766490617]\n",
+ "epoch 108, Pop max fitness [3.054386404315237e-06, 8.161932745674175e-05, 6.605892456070815e-05]\n",
+ "epoch 109, Pop max fitness [3.054386404315237e-06, 0.0003717472118959108, 0.0015625]\n",
+ "epoch 110, Pop max fitness [3.054386404315237e-06, 4.8666536889234965e-05, 5.883391186680002e-05]\n",
+ "epoch 111, Pop max fitness [3.054386404315237e-06, 8.33472245374229e-05, 0.001053740779768177]\n",
+ "epoch 112, Pop max fitness [3.054386404315237e-06, 2.6375481352534682e-05, 0.0002063557573256294]\n",
+ "epoch 113, Pop max fitness [3.054386404315237e-06, 4.5964331678617394e-05, 0.00014771048744460856]\n",
+ "epoch 114, Pop max fitness [3.054386404315237e-06, 3.5923411287135827e-05, 6.952169076751947e-05]\n",
+ "epoch 115, Pop max fitness [3.054386404315237e-06, 4.19533478771606e-05, 5.1535765821480104e-05]\n",
+ "epoch 116, Pop max fitness [3.054386404315237e-06, 4.32264199878966e-05, 0.0002206531332744925]\n",
+ "epoch 117, Pop max fitness [3.054386404315237e-06, 1.3482540110556829e-05, 5.972288580984233e-05]\n",
+ "epoch 118, Pop max fitness [3.054386404315237e-06, 9.675484257987112e-06, 0.004310344827586207]\n",
+ "epoch 119, Pop max fitness [3.054386404315237e-06, 0.00019554165037152912, 0.000141622999575131]\n",
+ "epoch 120, Pop max fitness [3.054386404315237e-06, 4.211590296495957e-05, 9.664917316632357e-06]\n",
+ "epoch 121, Pop max fitness [3.054386404315237e-06, 0.00018165304268846503, 9.072352007257882e-06]\n",
+ "epoch 122, Pop max fitness [3.054386404315237e-06, 4.2780748663101606e-05, 8.56677803478112e-05]\n",
+ "epoch 123, Pop max fitness [3.054386404315237e-06, 1.598261091931978e-05, 0.0016260162601626016]\n",
+ "epoch 124, Pop max fitness [3.054386404315237e-06, 1.8243847262510718e-05, 5.79609343302614e-05]\n",
+ "epoch 125, Pop max fitness [3.054386404315237e-06, 0.00020755500207555002, 4.9137634514274483e-05]\n",
+ "epoch 126, Pop max fitness [3.054386404315237e-06, 7.237984944991314e-05, 0.00018165304268846503]\n",
+ "epoch 127, Pop max fitness [3.054386404315237e-06, 3.936078091789341e-05, 4.154031487558676e-05]\n",
+ "epoch 128, Pop max fitness [3.054386404315237e-06, 5.245488879563575e-05, 0.008403361344537815]\n",
+ "epoch 129, Pop max fitness [3.054386404315237e-06, 4.5124317494697894e-05, 0.0002003606491685033]\n",
+ "epoch 130, Pop max fitness [3.054386404315237e-06, 0.00021767522855898998, 0.00036483035388544326]\n",
+ "epoch 131, Pop max fitness [3.054386404315237e-06, 0.00018846588767433095, 0.00018155410312273057]\n",
+ "epoch 132, Pop max fitness [3.054386404315237e-06, 0.0001142334932602239, 0.00013245033112582781]\n",
+ "epoch 133, Pop max fitness [3.054386404315237e-06, 6.697923643670462e-05, 0.0001389274798555154]\n",
+ "epoch 134, Pop max fitness [3.054386404315237e-06, 0.000145623998835008, 0.000326477309826967]\n",
+ "epoch 135, Pop max fitness [3.054386404315237e-06, 0.0003237293622531564, 0.0001311131506490101]\n",
+ "epoch 136, Pop max fitness [3.054386404315237e-06, 0.00030959752321981426, 0.00015715857300015716]\n",
+ "epoch 137, Pop max fitness [3.054386404315237e-06, 0.0001088139281828074, 0.00023391812865497077]\n",
+ "epoch 138, Pop max fitness [3.054386404315237e-06, 0.00011291779584462511, 0.00039888312724371757]\n",
+ "epoch 139, Pop max fitness [3.054386404315237e-06, 7.634753397465261e-05, 0.00025043826696719256]\n",
+ "epoch 140, Pop max fitness [3.054386404315237e-06, 0.0005787037037037037, 0.00018971732119142478]\n",
+ "epoch 141, Pop max fitness [3.054386404315237e-06, 5.787706910522051e-05, 0.00018953752843062926]\n",
+ "epoch 142, Pop max fitness [3.054386404315237e-06, 0.00023084025854108956, 0.00043159257660768235]\n",
+ "epoch 143, Pop max fitness [3.054386404315237e-06, 0.0005402485143165856, 9.991008092716555e-05]\n",
+ "epoch 144, Pop max fitness [3.054386404315237e-06, 0.0004033884630899556, 0.0002872737719046251]\n",
+ "epoch 145, Pop max fitness [3.054386404315237e-06, 0.00017927572606669058, 0.00034423407917383823]\n",
+ "epoch 146, Pop max fitness [3.054386404315237e-06, 9.128251939753537e-05, 0.0008445945945945946]\n",
+ "epoch 147, Pop max fitness [3.054386404315237e-06, 0.00011188185276348176, 0.0008445945945945946]\n",
+ "epoch 148, Pop max fitness [3.054386404315237e-06, 0.0002105706464518846, 0.0006093845216331506]\n",
+ "epoch 149, Pop max fitness [3.054386404315237e-06, 2.7544415369783778e-05, 8.028259473346178e-05]\n",
+ "epoch 150, Pop max fitness [3.054386404315237e-06, 0.017543859649122806, 0.0004923682914820286]\n",
+ "epoch 151, Pop max fitness [3.054386404315237e-06, 2.8447074218416637e-05, 0.00039603960396039607]\n",
+ "epoch 152, Pop max fitness [3.054386404315237e-06, 3.260940455227288e-05, 0.0005717552887364208]\n",
+ "epoch 153, Pop max fitness [3.054386404315237e-06, 3.950539248607435e-05, 0.00043047783039173483]\n",
+ "epoch 154, Pop max fitness [3.054386404315237e-06, 6.215426689042202e-05, 0.001326259946949602]\n",
+ "epoch 155, Pop max fitness [3.054386404315237e-06, 3.238132245320899e-05, 0.002136752136752137]\n",
+ "epoch 156, Pop max fitness [3.054386404315237e-06, 2.56285399420795e-05, 0.0005747126436781609]\n",
+ "epoch 157, Pop max fitness [3.054386404315237e-06, 2.489481938808534e-05, 0.0006493506493506494]\n",
+ "epoch 158, Pop max fitness [3.054386404315237e-06, 1.3614147822417056e-05, 0.0006397952655150352]\n",
+ "epoch 159, Pop max fitness [3.054386404315237e-06, 0.00011215791834903544, 0.0004894762604013706]\n",
+ "epoch 160, Pop max fitness [3.054386404315237e-06, 2.4716379544724288e-05, 0.0010030090270812437]\n",
+ "epoch 161, Pop max fitness [3.054386404315237e-06, 4.94486475794887e-05, 0.00019976028765481422]\n",
+ "epoch 162, Pop max fitness [3.054386404315237e-06, 0.0001336719689881032, 0.0001915341888527102]\n",
+ "epoch 163, Pop max fitness [3.054386404315237e-06, 0.00011821728336682824, 0.0001414227124876255]\n",
+ "epoch 164, Pop max fitness [3.054386404315237e-06, 4.4093654923056575e-05, 0.00011841326228537596]\n",
+ "epoch 165, Pop max fitness [3.054386404315237e-06, 0.00015865460891638903, 0.00033467202141900936]\n",
+ "epoch 166, Pop max fitness [3.054386404315237e-06, 0.00014369880729989942, 0.000544069640914037]\n",
+ "epoch 167, Pop max fitness [3.054386404315237e-06, 0.00011024142872891633, 0.004608294930875576]\n",
+ "epoch 168, Pop max fitness [3.054386404315237e-06, 1.0543629539032516e-05, 0.00014511681903932666]\n",
+ "epoch 169, Pop max fitness [3.054386404315237e-06, 1.888788153520701e-05, 0.00014150275930380644]\n",
+ "epoch 170, Pop max fitness [3.054386404315237e-06, 1.4950364788900849e-05, 0.00032615786040443573]\n",
+ "epoch 171, Pop max fitness [3.054386404315237e-06, 8.831113780069943e-06, 0.00013458950201884252]\n",
+ "epoch 172, Pop max fitness [3.054386404315237e-06, 4.468674591116275e-05, 0.0002538071065989848]\n",
+ "epoch 173, Pop max fitness [3.054386404315237e-06, 4.801690194948622e-05, 9.476876421531463e-05]\n",
+ "epoch 174, Pop max fitness [3.054386404315237e-06, 6.519754857217368e-05, 6.665777896280495e-05]\n",
+ "epoch 175, Pop max fitness [3.054386404315237e-06, 0.00014302059496567506, 0.001049317943336831]\n",
+ "epoch 176, Pop max fitness [3.054386404315237e-06, 0.00010195758564437195, 0.0006825938566552901]\n",
+ "epoch 177, Pop max fitness [3.054386404315237e-06, 2.654702806020866e-05, 0.0012121212121212121]\n",
+ "epoch 178, Pop max fitness [3.054386404315237e-06, 2.6551257202028515e-05, 0.00029824038174768865]\n",
+ "epoch 179, Pop max fitness [3.054386404315237e-06, 2.5178769261758485e-05, 0.00029824038174768865]\n",
+ "epoch 180, Pop max fitness [3.054386404315237e-06, 0.000164446637066272, 0.0007230657989877079]\n",
+ "epoch 181, Pop max fitness [3.054386404315237e-06, 0.0001556420233463035, 0.0001839249586168843]\n",
+ "epoch 182, Pop max fitness [3.054386404315237e-06, 9.418856550814731e-05, 0.00018138944313440957]\n",
+ "epoch 183, Pop max fitness [3.054386404315237e-06, 0.00016608536787908985, 0.0005878894767783657]\n",
+ "epoch 184, Pop max fitness [3.054386404315237e-06, 0.00027616680475006904, 0.0005858230814294083]\n",
+ "epoch 185, Pop max fitness [3.054386404315237e-06, 0.00016231131309852296, 0.0003901677721420211]\n",
+ "epoch 186, Pop max fitness [3.054386404315237e-06, 6.362942224484602e-05, 0.00024455857177794083]\n",
+ "epoch 187, Pop max fitness [3.054386404315237e-06, 2.8184098531608467e-05, 0.0004945598417408506]\n",
+ "epoch 188, Pop max fitness [3.054386404315237e-06, 2.862294988121476e-05, 0.00022634676324128565]\n",
+ "epoch 189, Pop max fitness [3.054386404315237e-06, 4.8243921265920496e-05, 0.00014742739200943535]\n",
+ "epoch 190, Pop max fitness [3.054386404315237e-06, 4.8225308641975306e-05, 4.808617041738796e-05]\n",
+ "epoch 191, Pop max fitness [3.054386404315237e-06, 0.00023912003825920613, 0.00012100677637947725]\n",
+ "epoch 192, Pop max fitness [3.054386404315237e-06, 0.00010955302366345311, 6.237135907191417e-05]\n",
+ "epoch 193, Pop max fitness [3.054386404315237e-06, 0.0004987531172069825, 5.7178798101663906e-05]\n",
+ "epoch 194, Pop max fitness [3.054386404315237e-06, 8.100445524503848e-05, 0.00010772379618657762]\n",
+ "epoch 195, Pop max fitness [3.054386404315237e-06, 5.6863414079381326e-05, 0.014285714285714285]\n",
+ "epoch 196, Pop max fitness [3.054386404315237e-06, 9.966115208291808e-05, 0.003937007874015748]\n",
+ "epoch 197, Pop max fitness [3.054386404315237e-06, 5.43980851874014e-05, 0.000244140625]\n",
+ "epoch 198, Pop max fitness [3.054386404315237e-06, 0.0005015045135406219, 0.00021043771043771043]\n",
+ "epoch 199, Pop max fitness [3.054386404315237e-06, 0.00014269406392694063, 5.7597051030987213e-05]\n",
+ "epoch 200, Pop max fitness [3.054386404315237e-06, 8.709284096847239e-05, 5.477051155657794e-05]\n",
+ "epoch 201, Pop max fitness [3.054386404315237e-06, 0.0012658227848101266, 8.305647840531562e-05]\n",
+ "epoch 202, Pop max fitness [3.054386404315237e-06, 2.1909644625564174e-05, 0.0018018018018018018]\n",
+ "epoch 203, Pop max fitness [3.054386404315237e-06, 2.3614981344164738e-05, 0.001941747572815534]\n",
+ "epoch 204, Pop max fitness [3.054386404315237e-06, 0.0007680491551459293, 0.0013003901170351106]\n",
+ "epoch 205, Pop max fitness [3.054386404315237e-06, 6.309148264984228e-05, 4.686255213458925e-05]\n",
+ "epoch 206, Pop max fitness [3.054386404315237e-06, 4.163544008660171e-05, 6.113590511707526e-05]\n",
+ "epoch 207, Pop max fitness [3.054386404315237e-06, 1.3342762218634503e-05, 0.0013157894736842105]\n",
+ "epoch 208, Pop max fitness [3.054386404315237e-06, 1.7354181490030022e-05, 0.00015192950470981463]\n",
+ "epoch 209, Pop max fitness [3.054386404315237e-06, 0.0002605523710265763, 0.0004068348250610252]\n",
+ "epoch 210, Pop max fitness [3.054386404315237e-06, 0.00011841326228537596, 4.1221814584278e-05]\n",
+ "epoch 211, Pop max fitness [3.054386404315237e-06, 0.00013356484573260317, 0.0004730368968779565]\n",
+ "epoch 212, Pop max fitness [3.054386404315237e-06, 9.994003597841295e-05, 1.0000000000000001e+45]\n",
+ "Finished phase Phase_0\n",
+ "--------------------------------\n",
+ "Evolutionary phase Phase_0\n",
+ "epoch 0, Pop max fitness [4.3651729044987475e-06, 0.00021258503401360543, 6.156119182467373e-05]\n",
+ "epoch 1, Pop max fitness [1.555451858764971e-05, 0.0013698630136986301, 4.5093795093795094e-05]\n",
+ "epoch 2, Pop max fitness [1.555451858764971e-05, 7.09622480840193e-05, 6.172077521293668e-05]\n",
+ "epoch 3, Pop max fitness [1.5557422446249105e-05, 0.00016989466530750936, 6.942515967786726e-05]\n",
+ "epoch 4, Pop max fitness [1.5557422446249105e-05, 2.896451846488052e-05, 9.931472837421789e-05]\n",
+ "epoch 5, Pop max fitness [1.5557422446249105e-05, 0.0005701254275940707, 0.00017571604287471446]\n",
+ "epoch 6, Pop max fitness [1.5557422446249105e-05, 0.0005512679162072767, 0.0019342359767891683]\n",
+ "epoch 7, Pop max fitness [1.5557422446249105e-05, 5.562044607597753e-05, 8.545547769612032e-05]\n",
+ "epoch 8, Pop max fitness [1.5557422446249105e-05, 0.00010036130068245685, 0.0001315097317201473]\n",
+ "epoch 9, Pop max fitness [1.5557422446249105e-05, 0.0006203473945409429, 0.000243842965130456]\n",
+ "epoch 10, Pop max fitness [1.5557422446249105e-05, 2.709072684420123e-05, 7.34322220590395e-05]\n",
+ "epoch 11, Pop max fitness [1.5557422446249105e-05, 1.1693444654926448e-05, 7.172572084349448e-05]\n",
+ "epoch 12, Pop max fitness [1.5557422446249105e-05, 1.548467017652524e-05, 9.299730307821073e-05]\n",
+ "epoch 13, Pop max fitness [1.5557422446249105e-05, 2.6752273943285178e-05, 4.260032376246059e-05]\n",
+ "epoch 14, Pop max fitness [1.5557422446249105e-05, 0.0008271298593879239, 5.447809980387884e-05]\n",
+ "epoch 15, Pop max fitness [1.5557422446249105e-05, 0.0007616146230007616, 0.0003448275862068965]\n",
+ "epoch 16, Pop max fitness [1.5557422446249105e-05, 0.0018315018315018315, 0.00012202562538133008]\n",
+ "epoch 17, Pop max fitness [1.5557422446249105e-05, 9.399379640943698e-05, 3.609326499675161e-05]\n",
+ "epoch 18, Pop max fitness [1.5557422446249105e-05, 0.00012938284383490748, 4.846370068818455e-05]\n",
+ "epoch 19, Pop max fitness [1.5557422446249105e-05, 0.00017027073046143367, 6.732193348592971e-05]\n",
+ "epoch 20, Pop max fitness [1.5557422446249105e-05, 0.0027247956403269754, 0.00016818028927009755]\n",
+ "epoch 21, Pop max fitness [1.5557422446249105e-05, 0.00012146240738491437, 8.589589417625838e-05]\n",
+ "epoch 22, Pop max fitness [1.5557422446249105e-05, 0.0008097165991902834, 4.501665616278023e-05]\n",
+ "epoch 23, Pop max fitness [1.5557422446249105e-05, 3.9022867400296575e-05, 0.00010715816545220746]\n",
+ "epoch 24, Pop max fitness [1.5557422446249105e-05, 2.025972973520533e-05, 0.0003508771929824561]\n",
+ "epoch 25, Pop max fitness [1.5557422446249105e-05, 8.602890571231934e-05, 0.0001199616122840691]\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Process ForkProcess-9582:\n",
+ "Process ForkProcess-9599:\n",
+ "Process ForkProcess-9583:\n",
+ "Process ForkProcess-9598:\n",
+ "Process ForkProcess-9563:\n",
+ "Process ForkProcess-9572:\n",
+ "Process ForkProcess-9597:\n",
+ "Process ForkProcess-9589:\n",
+ "Traceback (most recent call last):\n",
+ "Process ForkProcess-9595:\n",
+ "Process ForkProcess-9561:\n",
+ "Process ForkProcess-9585:\n",
+ "Process ForkProcess-9571:\n",
+ "Process ForkProcess-9576:\n",
+ "Process ForkProcess-9573:\n",
+ "Process ForkProcess-9588:\n",
+ "Process ForkProcess-9567:\n",
+ "Process ForkProcess-9564:\n",
+ "Process ForkProcess-9579:\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Process ForkProcess-9592:\n",
+ "Process ForkProcess-9565:\n",
+ "Process ForkProcess-9586:\n",
+ "Process ForkProcess-9596:\n",
+ "Process ForkProcess-9575:\n",
+ "Process ForkProcess-9577:\n",
+ "Process ForkProcess-9580:\n",
+ "Process ForkProcess-9574:\n",
+ "Process ForkProcess-9566:\n",
+ "Process ForkProcess-9569:\n",
+ "Process ForkProcess-9578:\n",
+ "Process ForkProcess-9568:\n",
+ "Traceback (most recent call last):\n",
+ "Process ForkProcess-9581:\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Process ForkProcess-9590:\n",
+ "Traceback (most recent call last):\n",
+ "Process ForkProcess-9570:\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Process ForkProcess-9594:\n",
+ "Traceback (most recent call last):\n",
+ "Process ForkProcess-9593:\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Process ForkProcess-9584:\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ "Process ForkProcess-9600:\n",
+ "Traceback (most recent call last):\n",
+ "Process ForkProcess-9587:\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ "Process ForkProcess-9591:\n",
+ "Traceback (most recent call last):\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 122, in get\n",
+ " return _ForkingPickler.loads(res)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+ " self.run()\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/process.py\", line 108, in run\n",
+ " self._target(*self._args, **self._kwargs)\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/home/juppy/venv_evolution/lib64/python3.11/site-packages/numpy/random/_pickle.py\", line 58, in __generator_ctor\n",
+ " return Generator(bit_generator_ctor(bit_generator_name))\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/concurrent/futures/process.py\", line 244, in _process_worker\n",
+ " call_item = call_queue.get(block=True)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+ " with self._rlock:\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ "KeyboardInterrupt\n",
+ " File \"/usr/lib64/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+ " return self._semlock.__enter__()\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^\n"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[1], line 86\u001b[0m\n\u001b[1;32m 82\u001b[0m controller()\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 86\u001b[0m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+ "Cell \u001b[0;32mIn[1], line 82\u001b[0m, in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 79\u001b[0m controller()\n\u001b[1;32m 80\u001b[0m \u001b[38;5;66;03m# for no mpi the main process is the controller\u001b[39;00m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 82\u001b[0m \u001b[43mcontroller\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n",
+ "Cell \u001b[0;32mIn[1], line 32\u001b[0m, in \u001b[0;36mcontroller\u001b[0;34m()\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28mprint\u001b[39m(GAController\u001b[38;5;241m.\u001b[39mpopulations[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mindividuals[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mgenome)\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# evolution for all phases\u001b[39;00m\n\u001b[0;32m---> 32\u001b[0m \u001b[43mGAController\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEvolve finished\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFirst best genes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+ "File \u001b[0;32m~/hostshared/pygma/src/controller.py:232\u001b[0m, in \u001b[0;36mController.evolve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__apply_operator_stack()\n\u001b[1;32m 228\u001b[0m \u001b[38;5;66;03m# instantiate individuals (get them a fitness value)\u001b[39;00m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;66;03m# tell them the phase index becaus the worker processes\u001b[39;00m\n\u001b[1;32m 230\u001b[0m \u001b[38;5;66;03m# (mpi and local) will have their instance of the\u001b[39;00m\n\u001b[1;32m 231\u001b[0m \u001b[38;5;66;03m# phase list and with it know the experiments.\u001b[39;00m\n\u001b[0;32m--> 232\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__instantiate_individuals\u001b[49m\u001b[43m(\u001b[49m\u001b[43mphase_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mp_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;66;03m# check if algorithm stagnates\u001b[39;00m\n\u001b[1;32m 235\u001b[0m \u001b[38;5;66;03m# IMP\u001b[39;00m\n\u001b[1;32m 236\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[38;5;66;03m# ----------\u001b[39;00m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;66;03m# information\u001b[39;00m\n\u001b[1;32m 244\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mepoch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mepochs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Pop max fitness \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_population_fitness()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
+ "File \u001b[0;32m~/hostshared/pygma/src/controller.py:96\u001b[0m, in \u001b[0;36mController.__instantiate_individuals\u001b[0;34m(self, phase_index)\u001b[0m\n\u001b[1;32m 94\u001b[0m futures \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 95\u001b[0m \u001b[38;5;66;03m# starting a worker for each individual\u001b[39;00m\n\u001b[0;32m---> 96\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mProcessPoolExecutor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnum_local_processes\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 97\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mw_tuple\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mworker_tuples\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[43m \u001b[49m\u001b[43mfutures\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_instantiate_individual_local_process\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/usr/lib64/python3.11/concurrent/futures/_base.py:647\u001b[0m, in \u001b[0;36mExecutor.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m 646\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, exc_type, exc_val, exc_tb):\n\u001b[0;32m--> 647\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshutdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwait\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 648\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+ "File \u001b[0;32m/usr/lib64/python3.11/concurrent/futures/process.py:825\u001b[0m, in \u001b[0;36mProcessPoolExecutor.shutdown\u001b[0;34m(self, wait, cancel_futures)\u001b[0m\n\u001b[1;32m 822\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executor_manager_thread_wakeup\u001b[38;5;241m.\u001b[39mwakeup()\n\u001b[1;32m 824\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executor_manager_thread \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m wait:\n\u001b[0;32m--> 825\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_executor_manager_thread\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 826\u001b[0m \u001b[38;5;66;03m# To reduce the risk of opening too many files, remove references to\u001b[39;00m\n\u001b[1;32m 827\u001b[0m \u001b[38;5;66;03m# objects that use file descriptors.\u001b[39;00m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executor_manager_thread \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[0;32m/usr/lib64/python3.11/threading.py:1112\u001b[0m, in \u001b[0;36mThread.join\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot join current thread\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1112\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wait_for_tstate_lock\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1113\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1114\u001b[0m \u001b[38;5;66;03m# the behavior of a negative timeout isn't documented, but\u001b[39;00m\n\u001b[1;32m 1115\u001b[0m \u001b[38;5;66;03m# historically .join(timeout=x) for x<0 has acted as if timeout=0\u001b[39;00m\n\u001b[1;32m 1116\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wait_for_tstate_lock(timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmax\u001b[39m(timeout, \u001b[38;5;241m0\u001b[39m))\n",
+ "File \u001b[0;32m/usr/lib64/python3.11/threading.py:1132\u001b[0m, in \u001b[0;36mThread._wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 1131\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mlock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 1133\u001b[0m lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m 1134\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stop()\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "from config import CONFIG, check_config\n",
+ "from controller import Controller\n",
+ "import numpy as np\n",
+ "\n",
+ "# remove here\n",
+ "def bool2int(x):\n",
+ " r = 0\n",
+ " for i, b in enumerate(x):\n",
+ " if b: r += b<<i \n",
+ " return r\n",
+ "\n",
+ "def mpi_worker():\n",
+ " # IMP:\n",
+ " # logic for the mpi worker\n",
+ "\n",
+ " return 0\n",
+ "\n",
+ "\n",
+ "def controller():\n",
+ " \"\"\"\n",
+ " Main controller logic for the algorithm\n",
+ " It will be called if the calling process shall be the controller.\n",
+ " \"\"\"\n",
+ " # Init controller\n",
+ " GAController = Controller(CONFIG, rng_seed=9)\n",
+ " print('Init controller')\n",
+ " print(GAController.populations)\n",
+ " print(GAController.populations[0].genetic_operator_stack)\n",
+ " print(GAController.populations[0].individuals[0].genome)\n",
+ " \n",
+ " # evolution for all phases\n",
+ " GAController.evolve()\n",
+ " print('Evolve finished')\n",
+ " \n",
+ " print('First best genes')\n",
+ " # todo find the population with the best gene\n",
+ " print(GAController.populations[0].individuals[0].fitness)\n",
+ " print(GAController.populations[0].individuals[1].fitness)\n",
+ " \n",
+ " print(GAController.populations[0].individuals[0].genome)\n",
+ " gene = GAController.populations[0].individuals[0].genome\n",
+ " numbers = np.split(gene, 3)\n",
+ " # convert the numbers to ints\n",
+ " x = bool2int(numbers[0])\n",
+ " y = bool2int(numbers[1])\n",
+ " z = bool2int(numbers[2])\n",
+ " \n",
+ " print(f'{x} , {y}, {z}')\n",
+ " print(x*2 + y*3 + z)\n",
+ " \n",
+ " # epoch loop\n",
+ " #GAController.epoch()\n",
+ " #print('Epoch')\n",
+ " #print(GAController.populations[0].individuals[0].genome)\n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " return\n",
+ "\n",
+ "\n",
+ "def main():\n",
+ " # read config\n",
+ " check_config()\n",
+ " config = CONFIG\n",
+ "\n",
+ " # check if MPI should be used\n",
+ " if config[\"use_mpi\"]:\n",
+ " comm = MPI.COMM_WORLD\n",
+ " rank = comm.Get_rank()\n",
+ " rank = 0\n",
+ "\n",
+ " # if mpi worker switch to worker mode\n",
+ " if rank != 0:\n",
+ " mpi_worker()\n",
+ " \n",
+ " # rank 0 is the controller\n",
+ " else:\n",
+ " controller()\n",
+ " # for no mpi the main process is the controller\n",
+ " else:\n",
+ " controller()\n",
+ " return\n",
+ "\n",
+ "\n",
+ "main()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adbd5d14-c9e9-464f-93ef-9cd43687f61d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "errir"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0555b2ad-8795-493d-b5ce-b3ea9d53ed82",
+ "metadata": {},
+ "source": [
+ "# code testing\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b0ef895-c6dd-4dac-a25a-0cef85b32b18",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1608e77-3c94-41bf-9d62-a74abc69cf0e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "np.binary_repr(12)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20500045-7cd1-4bf8-8dcf-ad8f4670e01e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "rng = np.random.default_rng(9)\n",
+ "a = rng.integers(low=0, high=2, size=10, dtype=np.bool_)\n",
+ "a.dtype"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18e861ff-db8e-417e-ba86-ace6f73dd457",
+ "metadata": {},
+ "source": [
+ "# boolean operators"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c1d8c25-f148-49ac-9494-c9dfd2284609",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "a = np.ones(4, dtype=np.bool_)\n",
+ "b = np.ones(4, dtype=np.bool_)\n",
+ "b[0] = 0\n",
+ "\n",
+ "# and\n",
+ "print(a & b)\n",
+ "# or\n",
+ "print(a|b)\n",
+ "# not\n",
+ "print(~b)\n",
+ "# xor\n",
+ "print(a ^ b)\n",
+ "# shift left\n",
+ "print(0b001 << 2)\n",
+ "# shift right\n",
+ "print(0b100 >> 2)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c879c3b6-9593-4334-aa1f-9f26395ba4b2",
+ "metadata": {},
+ "source": [
+ "# boolean conversion to integers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0900d64-f2ca-45f2-8a20-cac71797c3fa",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# for floats see\n",
+ "# https://stackoverflow.com/questions/14504323/how-can-i-convert-32-bit-binary-number-to-floating-point-number\n",
+ "\n",
+ "# used from here\n",
+ "# https://stackoverflow.com/questions/15505514/binary-numpy-array-to-list-of-integers\n",
+ "def bool2int(x):\n",
+ " r = 0\n",
+ " for i, b in enumerate(x):\n",
+ " if b: r += b<<i \n",
+ " return r\n",
+ "\n",
+ "a = np.ones(4, dtype=np.bool_)\n",
+ "a[1:3] = 0 \n",
+ "print(a)\n",
+ "bool2int(a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "03e1e23b-5f8b-4bea-afbe-2c0cada617a4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gene = np.array([0,0,1], dtype=np.bool_)\n",
+ "mutation_rate = 0.4\n",
+ "int(mutation_rate*gene.size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "04c422f1-19c7-4c7f-8cda-1981afaca1a3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rng = np.random.default_rng(9)\n",
+ "\n",
+ "indexes = rng.integers(low=0, high=gene.size, size=int(mutation_rate*gene.size))\n",
+ "indexes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b345398c-871c-4884-bbcd-144435e6ced6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "mutants = rng.integers(low=0, high=2, size=indexes.size, dtype=np.bool_)\n",
+ "mutants"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2eb76c18-c754-4c69-860b-e399f7d3129a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "gene"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f04c0eb4-1f44-4c18-ba5d-522db27ebd53",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "gene[indexes] = not gene[indexes]\n",
+ "gene"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "91504cbb-111e-4006-834e-cd78e592c8b9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from genetic_operators import Mutation_operator\n",
+ "\n",
+ "\n",
+ "a = np.array([0,0,1], dtype=np.bool_)\n",
+ "b = np.array([1,0,1], dtype=np.bool_)\n",
+ "p_old = [a.copy(),b.copy()]\n",
+ "p_new = [a,b]\n",
+ "m = Mutation_operator(1)\n",
+ "n = m.operate(p_old, p_new)\n",
+ "n = m.operate(p_old, n)\n",
+ "n = m.operate(p_old, n)\n",
+ "n = m.operate(p_old, n)\n",
+ "n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "98c7c006-d09e-4a0f-832a-bde673d5dd1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "p_old"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "baa4bca8-884d-4ead-bb18-f00fe41b4c82",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "p_new"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cd541cdd-e8a4-42ba-8ce2-45f0774ca091",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "indexes = np.array([0,1])\n",
+ "a[indexes] = ~a[indexes]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a4eab6fc-8ff8-4287-80fb-16244b6d3304",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7c4b97f7-1dc4-42b8-a2e6-50cf7d9c787a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "a[indexes]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a94cdc0b-736a-4eb0-8ef3-6cf15dd85957",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "np.invert(a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "af49dbab-b3de-460e-a878-4999cf5d0821",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "a = [1,2,3,3,4]\n",
+ "b = [9,66]\n",
+ "\n",
+ "for i in zip(a,b):\n",
+ " print(i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "74b286ba-749b-4db9-ae23-5d474e0fdc13",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "a = np.array([0,0,1], dtype=np.bool_)\n",
+ "b = np.array([1,0,1], dtype=np.bool_)\n",
+ "\n",
+ "c = [a.copy(),a]\n",
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "02864e9f-302a-46f6-a1a8-1418e6fd08f4",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "c[0][0] = True\n",
+ "c"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "129c9a6f-84b8-412d-929f-2f190a7015be",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fe92f1af-d7b4-4332-82ae-e96fa9502fe3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "a.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "462895ce-3f6e-4a4c-a7aa-afd3952c514d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "a = np.array([0,0,1], dtype=np.bool_)\n",
+ "\n",
+ "np.array(a.copy(), dtype=np.bool_)\n",
+ "a.size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a61dfa16-4470-416d-823c-2bc383db1149",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "a = [1,2,3]\n",
+ "for n, i in enumerate(a):\n",
+ " print(n, i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "57908ff3-10b6-4fc0-83c8-0634f1982458",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from genetic_operators import Genetic_operator\n",
+ "\n",
+ "op = Mutation_operator()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7bc7544d-7b9a-4fb5-bde6-71897ee9c9e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rng = np.random.default_rng(0)\n",
+ "k_point = rng.integers(low=0, high=a.size, size=1)[0]\n",
+ "k_point"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96328a1f-2fe8-4316-991f-684218b3597b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "parent_1[k_point:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "43a66e83-7e10-470a-bdcd-7a696a917783",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "parent_0 = rng.integers(low=0, high=2, size=4, dtype=np.bool_)\n",
+ "parent_1 = rng.integers(low=0, high=2, size=4, dtype=np.bool_)\n",
+ "child = np.concatenate((parent_0[:k_point], parent_1[k_point:]))\n",
+ "child.dtype"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22685129-5bc2-448d-a36d-73a8925d8065",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "parent_0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "74793350-663d-4d28-ad0f-9ddee3efcc0e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "parent_1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0986ed69-676c-4d45-a93a-e61a09aeee97",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "a = [[1],[2],[3],[4]]\n",
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "33c55fbc-2631-4100-bc22-cef7e1cbd5de",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "num_genes_to_remove = 2\n",
+ "a[:len(a)-num_genes_to_remove]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4c45f577-39c2-4daf-a535-116a2a3fda95",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "a[num_genes_to_remove:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3a0db8f4-ce53-443c-b27e-202d704a29a1",
+ "metadata": {},
"outputs": [],
"source": []
}
diff --git a/src/config.py b/src/config.py
new file mode 100755
index 0000000..fbb9f28
--- /dev/null
+++ b/src/config.py
@@ -0,0 +1,105 @@
+from genetic_operators import Mutation_operator, Single_point_crossover_operator, Removal_operator
+from evolutionary_phase import Simple_evolutionary_phase
+from experiment import Function_optimisation_experiment
+
+
+# -----------------------------
+# Genetic operator definition
+# -----------------------------
+
+OP_MUTATION = Mutation_operator(0.06)
+OP_REMOVAL = Removal_operator(6)
+OP_CROSSOVER = Single_point_crossover_operator(6)
+
+
+# -----------------------------
+# Phase definition
+# -----------------------------
+# phase 0
+# phase starting operators (applied at phase start)
+PHASE0_SOP = [OP_MUTATION, OP_MUTATION]
+# phase Experiment (conducted on individuals)
+PHASE0_EXPERIMENT = Function_optimisation_experiment()
+PHASE0 = Simple_evolutionary_phase(
+ 'Phase_0', PHASE0_SOP, PHASE0_EXPERIMENT)
+
+# phase 1
+
+
+# -----------------------------
+# Configuration parameters
+# -----------------------------
+CONFIG = {
+ # -------------
+ # Processes
+ # --------------
+ # use mpi parallelization
+ 'use_mpi': False,
+
+ # if mpi=False, how many local parallel processe
+ 'num_local_processes': 1,
+
+
+ # -------------
+ # Populations
+ # --------------
+ # how many island populations to use
+ # is defined by the genetic operators stacks
+ # used to recombine/produce, mutate, extend
+ 'genetic_operator_stack': [
+ # pop 0
+ [OP_REMOVAL, OP_CROSSOVER],
+ # pop 1
+ [OP_REMOVAL, OP_CROSSOVER, OP_MUTATION],
+ # pop 2
+ [OP_REMOVAL, OP_REMOVAL, OP_REMOVAL, OP_MUTATION, OP_CROSSOVER, OP_CROSSOVER, OP_CROSSOVER]
+ ],
+
+ # how many island populations to use
+ # 'num_populations': 3,
+
+ # how many individuals per population
+ 'num_individuals': 30,
+
+ # genome lenght
+ 'genome_length': 60,
+
+ # init populations from defined gene strings
+ # this will ovveride:
+ # num_populations, num_individuals, genome_length
+ 'use_predefined_defined_genes': False,
+ 'predefined_genes': [
+ # pop 0
+ [[0, 0, 1], [0, 1, 1]],
+ # pop 1
+ [[0, 1, 1], [1, 1, 1]]
+ ],
+
+ # -------------
+ # Evolutinary Phases
+ # --------------
+ 'evolutionary_phases': [
+ PHASE0, PHASE0
+ ]
+
+}
+# -----------------------------
+# check configured parameters
+# -----------------------------
+
+
+def check_config():
+ # IMP
+ # will check if all parameters are available and within
+ # reasonable range
+ # really do this?
+ # only for most critical parts that have to fit.
+
+ # check if genetic operators are enough in every phase
+ if CONFIG['use_predefined_defined_genes'] and len(CONFIG['genetic_operator_stack']) != len(CONFIG['predefined_genes']):
+ print(
+ f"Warning: having an genetic operator stack for only {len(CONFIG['genetic_operator_stack'])} but there are genes defined for {len(CONFIG['predefined_genes'])}. Will not use all genes, only those having an operator stack.")
+
+ return
+
+ return
diff --git a/src/controller.py b/src/controller.py
new file mode 100755
index 0000000..905b701
--- /dev/null
+++ b/src/controller.py
@@ -0,0 +1,254 @@
+from population import Population
+from concurrent.futures import ProcessPoolExecutor
+import numpy as np
+# FIXME:
+# implement correct print functions in the classes
+# such that print(object) can be called
+# implement usefull controller output
+
+class Controller:
+ """
+ GA Controller.
+ Handels the algorithm logic.
+ """
+
+ def __init__(self, config, rng_seed=9):
+ self.config = config
+
+ # Population objects
+ self.populations = []
+
+ # random number generator
+ self.rng = np.random.default_rng(rng_seed)
+
+ # phases
+ # self.evolutional_phases = config['evolutionary_phases']
+
+ # init populations
+ self.__init_populations()
+
+ # epochs
+ self.epochs = 0
+
+ def __init_populations(self):
+ """
+ Initialize the populations
+ """
+ if self.config["use_predefined_defined_genes"]:
+ # make pops
+ for genetic_op_stack, genes in zip(self.config["genetic_operator_stack"], self.config["predefined_genes"]):
+ pop = Population(genetic_op_stack, self.rng)
+ pop.init_individuals(genomes=genes)
+ # append
+ self.populations.append(pop)
+
+ else:
+ for genetic_operator_stack in self.config["genetic_operator_stack"]:
+ # make population
+ pop = Population(genetic_operator_stack, self.rng)
+ pop.init_individuals(
+ num_individuals=self.config["num_individuals"],
+ genome_length=self.config["genome_length"],
+ )
+ # append
+ self.populations.append(pop)
+
+ def __instantiate_individuals(self, phase_index):
+ """
+ Will instantiate the individuals in the environment function specified
+ in the experiment of the phase.
+ Parameters
+ ----------
+ phase_index: int
+ the index of the pase list specified in the confid by the user.
+ Used to conduct the correct experiment for the phase.
+ """
+ # pass genes of all populations to the workers
+ # one idea is to pass the indexes, meaning:
+ # pass
+ # (pop_index, indiv_index, phase_index, gene)
+ # receive back
+ # (pop_index, indiv_index, fitness)
+ # then rewrite the fitness into the population individuals
+
+ # nicer would be to pass the individual itself and having the reference updated
+ # this will not work with mpi though
+
+ # this is not the nicest pythonic way with the indexes of the populations
+ # but it will work for now
+ # FIXME: find a more pythonic way
+
+ # make list that workers should evaluate
+ worker_tuples = []
+ for i_pop, pop in enumerate(self.populations):
+ for i_indiv, indiv in enumerate(pop.individuals):
+ worker_tuples.append(
+ (i_pop, i_indiv, phase_index, indiv.genome))
+
+ # instantiation via mpi or local worker
+ # ---------------------------------------
+ if self.config['use_mpi']:
+ raise NotImplementedError
+ else:
+ # save worker futures
+ futures = []
+ # starting a worker for each individual
+ with ProcessPoolExecutor(max_workers=self.config['num_local_processes']) as executor:
+ for w_tuple in worker_tuples:
+ futures.append(
+ executor.submit(
+ self._instantiate_individual_local_process,
+ pop_index=w_tuple[0],
+ indiv_index=w_tuple[1],
+ phase_index=w_tuple[2],
+ gene=w_tuple[3],
+ )
+ )
+
+ # write back the fitness values into the individuals
+ for future in futures:
+ # result will be a tuple (pop_index, indiv_index, fitness)
+ result = future.result()
+ self.populations[result[0]
+ ].individuals[result[1]].fitness = result[2]
+
+ # sort the popuPopulations based on their fitness values
+ for pop in self.populations:
+ pop.sort_individuals()
+
+ def _instantiate_individual_local_process(self, pop_index, indiv_index, phase_index, gene):
+ # FIXME:
+ # is it really the case that when a new worker is instantiated
+ # that it will get a full deep copy of all the objects?
+ # As such the experiment for an individual is called
+ # on a single, only by this worker used instance of the
+ # experiment right?
+ # I think so but cannot find the source (despite my brain) that
+ # verifyes that.
+ """
+ Will make an experiment with the gene
+ Parameters
+ ----------
+ pop_index: int
+ index of the population inside the controller population list
+ indiv_index: int
+ index of the individual inside its population
+ phase_index: int
+ index of the configuration phase array.
+ If you have the config you can get the experiment :)
+ gene: list
+ gene of the individual (numpy.array(dtype=numpy.bool_))
+ """
+ # instantiate the experiment and evaluate the individual
+ experiment = self.config['evolutionary_phases'][phase_index].experiment
+
+ # conduct the experiment
+ #print(pop_index)
+ #print('worker controller has',self.epochs,'epochs and is at',self)
+ #print(self)
+ fitness = experiment.conduct(gene)
+
+ # return result tuple
+ return (pop_index, indiv_index, fitness)
+
+ # not used
+ def epoch(self):
+ """
+ One epoch:
+ Instantiate individuals and evaluate their fitness.
+ Apply operator stack.
+ May combine island populations
+ """
+ # instantiate individuals (get them a fitness value)
+ self.__instantiate_individuals()
+
+ # genetic operator stack
+ # NOTE: after applying the stack the fitness values
+ # will be zero again for all individuals
+ self.__apply_operator_stack()
+
+ # fusion islands
+ # IMP
+
+ def __apply_operator_stack(self):
+ """
+ Applying the operator stack for each population.
+ """
+ for pop in self.populations:
+ pop.apply_operator_stack()
+
+ def get_population_fitness(self, mean=False) -> list:
+ """
+ Returns the fitness of all populations.
+ Parameters
+ ----------
+ mean: bool
+ if true return the mean fitness of the populations,
+ otherwise the max fitness value
+ """
+ f = []
+ for pop in self.populations:
+ f.append(pop.get_fitness(mean=mean))
+ return f
+
+ def evolve(self):
+ """
+ Evolves the genomes through all phases
+ """
+ for p_index, evolutionary_phase in enumerate(self.config['evolutionary_phases']):
+ # ----------
+ # init phase
+ # ----------
+ print(f'Evolutionary phase {evolutionary_phase}')
+ # reset epochs
+ self.epochs = 0
+
+ # call the phase init
+ # which will return a new population array
+ self.populations = evolutionary_phase.initialize(
+ self.populations)
+
+ # apply phase start operators
+ for op in evolutionary_phase.phase_start_operators:
+ for population in self.populations:
+ population.apply_operator(op)
+
+ # ----------
+ # phase epochs
+ # ----------
+ while not evolutionary_phase.completed(self.get_population_fitness(), self.epochs):
+ # conduct a new epoch
+ # -------------------
+
+ # apply genetic operator stack on each population
+ # NOTE: after applying the stack the fitness values
+ # will be zero again for all individuals
+ self.__apply_operator_stack()
+
+ # instantiate individuals (get them a fitness value)
+ # tell them the phase index becaus the worker processes
+ # (mpi and local) will have their instance of the
+ # phase list and with it know the experiments.
+ self.__instantiate_individuals(phase_index=p_index)
+
+ # check if algorithm stagnates
+ # IMP
+
+ # fusion islands but what is the condition?
+ # whenever a new phase happens the phase can do
+ # this on their own.
+
+ # epoch done
+ # ----------
+ # information
+ print(f'epoch {self.epochs}, Pop max fitness {self.get_population_fitness()}')
+
+ # increment counter
+ self.epochs += 1
+
+ # ----------
+ # finalize
+ # ----------
+ print(f'Finished phase {evolutionary_phase}')
+ print('--------------------------------')
+ #print('--------------------------------')
diff --git a/src/evolutionary_phase.py b/src/evolutionary_phase.py
new file mode 100755
index 0000000..0ec02a7
--- /dev/null
+++ b/src/evolutionary_phase.py
@@ -0,0 +1,61 @@
+from genetic_operators import Genetic_operator
+from experiment import Experiment
+from population import Population
+
+# --------------
+# Interface idea
+# --------------
+
+
+class Evolutionary_phase:
+ """
+ A phase in the evolutionary algorithm
+ This class serves as informal interface and has to be extended
+ """
+
+ def __init__(self, name, phase_start_operators, experiment):
+ self.name = name
+ self.phase_start_operators: Genetic_operator = phase_start_operators
+ self.experiment: Experiment = experiment
+
+ def __str__(self):
+ return self.name
+
+ def completed(self, population_fitness: list, epochs: int) -> bool:
+ """
+ Called to check if the phase has reached its end.
+ """
+ raise NotImplementedError
+
+ def initialize(self, populations: list) -> list:
+ """
+ Here one can combine fusion/recombine, change the genetic operators...
+ returns a list with the populations that shall be used from now on.
+ For example one could define new genetic operators and then apply
+ them by change the population.genetic_operator_stack = new_stack
+ """
+ # modifie popopulations
+ return populations
+
+# --------------
+# Phases
+# --------------
+
+
+class Simple_evolutionary_phase(Evolutionary_phase):
+
+ # def __init__(self, phase_start_operators, experiment):
+ # super().__init__(phase_start_operators, experiment)
+
+ def completed(self, population_fitness: list, epochs: int) -> bool:
+ """
+ Called to check if the phase has reached its end.
+ """
+ max_fitness = max(population_fitness)
+ if max_fitness > 0.9:
+ return True
+
+ if epochs > 500:
+ return True
+
+ return False
diff --git a/src/experiment.py b/src/experiment.py
new file mode 100755
index 0000000..979d901
--- /dev/null
+++ b/src/experiment.py
@@ -0,0 +1,92 @@
+import numpy as np
+# https://towardsdatascience.com/an-extensible-evolutionary-algorithm-example-in-python-7372c56a557b
+# traveling salesman problem here
+# --------------
+# Interface idea
+# --------------
+
+
+class Experiment:
+ """
+ Class defining an experiment.
+ """
+
+ def __init__(self):
+ self.inputs = []
+ self.expected_outputs = []
+ self.environment = None
+
+ def conduct(self, gene):
+ """
+ Conducts the experiment with the given gene
+ Returns the fitness value.
+ Parameters
+ ----------
+ gene: numpy.array(dtype=numpy.bool_)
+ For easy objectives just implement your function.
+ For complex objective functions involving simulations the
+ gene is instantiatet in an environment,
+ returning the phenotype from the genotype.
+ The phenotype can then be used to process the inputs and
+ give back its outputs.
+ """
+ # Idea
+ # -----
+ # instantiate individual
+ # individual = self.environment.instantiate_individual(gene)
+
+ # test the individual
+ # outputs = individual(self.inputs)
+ # if outputs == self.expected_outputs:
+ # return 0.999
+
+ # return 0.0
+
+ raise NotImplementedError
+
+# --------------
+# Experiments
+# --------------
+
+
+def bool2int(x):
+ r = 0
+ for i, b in enumerate(x):
+ if b:
+ r += b << i
+ return r
+
+
+class Function_optimisation_experiment(Experiment):
+ """
+ Find values for the function such that it matches a certain output
+ """
+
+ def f(self, x, y, z):
+ return x*2 + y*3 + z
+
+ def conduct(self, gene):
+ # split gene into blocks that are binary representation
+ # of the numbers
+ numbers = np.split(gene, 3)
+ # convert the numbers to ints
+ x = bool2int(numbers[0])
+ y = bool2int(numbers[1])
+ z = bool2int(numbers[2])
+
+ # define the objective output
+ output = 424242
+
+ # calculate the outpup
+ r = self.f(x, y, z)
+
+ # calc error and fitness
+ error = abs(r-output)
+
+ # if error is small fitness is high
+ if error == 0:
+ error = 0.000000000000000000000000000000000000000000001
+ fitness = 1/error
+
+ #print(f'error: {error} fitness {fitness}')
+ return fitness
diff --git a/src/genetic_operators.py b/src/genetic_operators.py
new file mode 100755
index 0000000..0b02c16
--- /dev/null
+++ b/src/genetic_operators.py
@@ -0,0 +1,135 @@
+import numpy as np
+
+# -----------------
+# Formal interface
+# -----------------
+# Write the idea into the docs that:
+# for the operatos the idea is that they always append new childs
+# into the population gene list.
+# as such they are able to not remove genes that where made by other
+# operators. If the population should be keep small apply the
+# population shrinkage operator which will shrink the new_population
+# in the first place so that now other operators can add new genes.
+# The user knows how much he adds and as such the stack shall always
+# come to no increase in the end.
+# e.g. remove(4), crossover(2), add_random(4)...
+
+
+class Genetic_operator():
+ """
+ This class will apply a genetic operator to a set of genes.
+ """
+
+ def operate(self, old_population, new_population):
+ """
+ Applys the genetic operator on the genes
+ Parameters
+ ----------
+ old_population: list of numpy.array(dtype=numpy.bool_) genes
+ genes before the operator stack is applied, sorted by their fitness
+ new_population: list
+ current state of modifications in operator stack
+ Note that if this is not the first operator which is applied
+ the actual fitness (the indexes, fitness_0 >= fitness_1) unclear.
+ Fitness is always represented at the point where
+ no operator was applied after an evaluation of the individuals.
+
+ return: list
+ new genes to be used, these have to be arrays of numpy booleans!
+ """
+
+ raise NotImplementedError
+
+# -----------------
+# Genetic Operators
+# -----------------
+
+
+class Mutation_operator(Genetic_operator):
+ """
+ This class represents a mutation operator.
+ """
+
+ def __init__(self, mutation_rate, rng_seed=9):
+ self.mutation_rate = mutation_rate
+ self.rng = np.random.default_rng(rng_seed)
+
+ def operate(self, old_population, new_population):
+ """
+ Apply mutation to all genes of the current op stack.
+ """
+ for gene in new_population:
+ # mutation indexes
+ # having at least one mutation
+ mutations = int(self.mutation_rate*gene.size)
+ if mutations == 0:
+ mutations = 1
+ indexes = self.rng.integers(low=0, high=gene.size, size=mutations)
+ # flip the bits at indexes
+ # note, ~ is like np.invert()
+ gene[indexes] = ~gene[indexes]
+ return new_population
+
+
+class Removal_operator(Genetic_operator):
+ """
+ This class represents a gene removal operator
+ """
+
+ def __init__(self, num_genes_to_remove, remove_last=True):
+ """
+ Parameters
+ ----------
+ num_genes_to_remove: int
+ number of genes to remove
+ remove_last: bool
+ remove the last or first individuals
+ since list is sorded by fitness remove_last
+ will remove the not fit ones
+ """
+ self.num_genes_to_remove = num_genes_to_remove
+ self.remove_last = remove_last
+
+ def operate(self, old_population, new_population):
+ """
+ Removes N genes from the actual gene stack
+ """
+ # remove
+ if self.remove_last:
+ return new_population[:len(new_population)-self.num_genes_to_remove]
+ return new_population[self.num_genes_to_remove:]
+
+
+class Single_point_crossover_operator(Genetic_operator):
+ """
+ This class represents a single point (random) crossover operator.
+ """
+
+ def __init__(self, num_new_offsprings, rng_seed=9):
+ """
+ Parameters
+ ----------
+ num_new_offsprings: int
+ number of new individuals made
+ """
+ self.num_offsprings = num_new_offsprings
+ self.rng = np.random.default_rng(rng_seed)
+
+ def operate(self, old_population, new_population):
+ """
+ Apply crossover operator with genes from the new population
+ and append the newly generated genes into the population.
+ """
+ # parent indexes in the population
+ pop_index = 0
+ for _ in range(self.num_offsprings):
+ # get parent genes
+ parent_0 = new_population[pop_index]
+ parent_1 = new_population[pop_index+1]
+ pop_index += 2
+ # have k point wise selection
+ k_point = self.rng.integers(low=0, high=parent_0.size, size=1)[0]
+ child = np.concatenate((parent_0[:k_point], parent_1[k_point:]))
+ # add gene to population
+ new_population.append(child)
+ return new_population
diff --git a/src/individual.py b/src/individual.py
new file mode 100755
index 0000000..49f4789
--- /dev/null
+++ b/src/individual.py
@@ -0,0 +1,7 @@
+import numpy as np
+
+
+class Individual():
+ def __init__(self, genome):
+ self.genome = np.array(genome, dtype=np.bool_)
+ self.fitness = 0
\ No newline at end of file
diff --git a/src/population.py b/src/population.py
new file mode 100755
index 0000000..286f94e
--- /dev/null
+++ b/src/population.py
@@ -0,0 +1,118 @@
+from individual import Individual
+from genetic_operators import Genetic_operator
+import numpy as np
+
+
+class Population:
+ def __init__(self, operator_stack, rng):
+ # self.num_individuals = 0
+ # self.gene_length = 0
+ self.rng = rng
+ # self.genomes= genomes
+
+ # individual objects
+ self.individuals: Individual = []
+
+ # genetic operators
+ self.genetic_operator_stack = operator_stack
+
+ def init_individuals(self, genomes=None, num_individuals=None, genome_length=None):
+ """
+ Initiates the individuales with the genomes given or creates n
+ random individuals with a specified genome length.
+
+ Parameters
+ ----------
+ genomes: numpy.array(dtype=numpy.bool_)
+ 2D array with the genomes
+ num_individuals: Int
+ how many random individuals
+ genome_length: Int
+ length of the genes
+ """
+ if genomes is None and (num_individuals is None or genome_length is None):
+ raise ValueError(
+ 'Provide a fixed genome or parameters to define them randomly (number and size)')
+ if genomes is not None:
+ for gene in genomes:
+ self.individuals.append(Individual(gene))
+ else:
+ for i in range(num_individuals):
+ genome = self.rng.integers(
+ low=0, high=2, size=genome_length, dtype=np.bool_)
+ self.individuals.append(Individual(genome))
+
+ def __get_gene_array(self):
+ """
+ Will return a list containing genome copies of all the individuals.
+ """
+ genes = []
+ for indiv in self.individuals:
+ # copy as otherwise will be modified (C-pointer)
+ # make sure that we really have a numpy bool array!
+ genes.append(np.array(indiv.genome.copy(), dtype=np.bool_))
+ return genes
+
+ def apply_operator_stack(self):
+ """
+ Applys the operator stack on this populations genes.
+ """
+
+ # operators getting the genes only
+ # they get a state before the stack and the current stack state
+ # hence we save all the genes in two lists and evolve the stack list
+ # NOTE: this are all numpy arrays, as such you can modified inplace.
+ # however operators might make new arrays and thus make new individuals.
+ unmodified_genes = self.__get_gene_array()
+ current_stack_state = self.__get_gene_array()
+
+ # apply stack
+ for genetic_operator in self.genetic_operator_stack:
+ current_stack_state = genetic_operator.operate(
+ unmodified_genes, current_stack_state)
+
+ # a new generation is born
+ self.individuals = []
+ for gene in current_stack_state:
+ self.individuals.append(Individual(gene))
+
+ def apply_operator(self, operator: Genetic_operator):
+ """
+ Applies the genetic operator on all genes of the population
+ """
+ genes = self.__get_gene_array()
+ op_genes = operator.operate(genes, genes)
+
+ # a new generation is born
+ # which might have more members then the last one
+ # hence create complepely new one
+ self.individuals = []
+ for gene in op_genes:
+ self.individuals.append(Individual(gene))
+
+ def get_fitness(self, mean=False):
+ """
+ Fitness of the population.
+ Parameters:
+ -----------
+ mean: bool
+ if True return the mean fitness
+ otherwise the max
+ """
+ # fitness list
+ f = []
+ for indiv in self.individuals:
+ f.append(indiv.fitness)
+
+ if mean:
+ return sum(f)/len(self.individuals)
+ return max(f)
+
+ def sort_individuals(self):
+ """
+ Sorts the individuals in the population based on their
+ fitness values.
+ """
+ self.individuals.sort(
+ key=lambda Individual: Individual.fitness, reverse=True
+ )
diff --git a/src/population_old.py b/src/population_old.py
new file mode 100755
index 0000000..e7e7360
--- /dev/null
+++ b/src/population_old.py
@@ -0,0 +1,33 @@
+from individual import Individual
+
+
+class Population:
+ def __init__(self, num_individuals, gene_length, rng, genomes=False):
+ self.num_individuals = num_individuals
+ self.gene_length = gene_length
+ self.rng = rng
+ #self.genomes= genomes
+
+ # individual objects
+ self.individuals = []
+
+ # init individuals randomly
+ # or with the provided genomes
+ if not genomes:
+ self.__init_individuals_random()
+ else:
+ self.__init_individuals_specific(genomes)
+
+ def __init_individuals_random(self):
+ # IMP
+ # how to initialize the individual genome length?
+ for i in range(self.num_individuals):
+ self.individuals.append(Individual())
+
+ def __init_individuals_specific(self, genomes):
+ """
+ initiates the individuales with the genomes given.
+ genomes: 2D array with the genomes
+ """
+ for gene in genomes:
+ self.individuals.append(Individual(gene))
diff --git a/src/testing_notebook.ipynb b/src/testing_notebook.ipynb
new file mode 100755
index 0000000..9a8e539
--- /dev/null
+++ b/src/testing_notebook.ipynb
@@ -0,0 +1,363 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "082d3205-bc4f-4528-99c7-ae716c5c75e7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a = [1,2]\n",
+ "max(a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "5660fbcf-e2a1-48f5-a63b-41c536e035ec",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "a.reverse()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "20d34eb6-8b29-4683-82ef-9639e346078b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[2, 1]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "cb10b989-95e3-4690-be2d-0728c29fa051",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[2, 3]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f = 0\n",
+ "[f := f + fit for fit in a]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "00e70d76-2d57-4721-b739-5567e49527cc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[1, 3, 6, 10, 15]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "total = 0\n",
+ "[total := total + x for x in [1, 2, 3, 4, 5]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "1527bef8-3bab-4fdf-8954-b02184ac7098",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10.0"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "30/3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "eb85f179-cc72-45bc-8b16-11c3b673172e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.00010001000100010001"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "1/9999\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "b8e273b2-9813-4ea9-8d0b-ef8815213d6d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ True False False True]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "# used from here\n",
+ "# https://stackoverflow.com/questions/15505514/binary-numpy-array-to-list-of-integers\n",
+ "def bool2int(x):\n",
+ " r = 0\n",
+ " for i, b in enumerate(x):\n",
+ " if b: r += b<<i \n",
+ " return r\n",
+ "\n",
+ "a = np.ones(4, dtype=np.bool_)\n",
+ "a[1:3] = 0 \n",
+ "print(a)\n",
+ "bool2int(a[:1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "ce76e4b9-f60c-4512-8c33-f836712e49fe",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ True])"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a[:1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "c84b7f89-fd76-45cf-8f99-5e54a4bdae18",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "9"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bool2int(a[:4])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "2c93a449-d92f-4440-9d09-6c23d9e06983",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "a = np.ones(15, dtype=np.bool_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "f2801797-a8c1-4346-8c3a-9ef3a5b43368",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[array([ True, True, True, True, True]),\n",
+ " array([ True, True, True, True, True]),\n",
+ " array([ True, True, True, True, True])]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "block_size = 3\n",
+ "numbers = np.split(a,3)\n",
+ "numbers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "0c9811f4-6044-495f-87ce-2d54e8b6e63a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a.astype(float)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "ca7f6d39-e4aa-461e-bcc5-d15b04b283c5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f = [1,2]\n",
+ "sum(f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "545df3e9-9fa6-44c6-b6a0-85707a316256",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a = [3,9,6]\n",
+ "a\n",
+ "sorted(student_objects, key=lambda student: student.age)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv_evolution",
+ "language": "python",
+ "name": "venv_evolution"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
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