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VERSION 0.6.1
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blankjul committed Nov 19, 2023
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1 change: 1 addition & 0 deletions .pre-commit-config.yaml
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- --preserve-cell-metadata
- tags
- format
- raw_mimetype
- --
8 changes: 6 additions & 2 deletions docs/source/algorithms/hyperparameters.ipynb
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"cells": [
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".. _nb_algorithms_hyperparameters:"
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},
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"source": [
".. admonition:: Info\n",
" :class: myOwnStyle\n",
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12 changes: 9 additions & 3 deletions docs/source/algorithms/index.ipynb
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"cells": [
{
"cell_type": "raw",
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"source": [
".. _nb_algorithms:"
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},
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"source": [
".. toctree::\n",
" :hidden:\n",
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},
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"source": [
".. admonition:: Overview\n",
" :class: myOwnStyle\n",
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8 changes: 6 additions & 2 deletions docs/source/algorithms/initialization.ipynb
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"cells": [
{
"cell_type": "raw",
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"source": [
".. _nb_algorithms_init:"
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from pymoo.algorithms.moo.nsga2 import NSGA2\n",
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8 changes: 6 additions & 2 deletions docs/source/algorithms/list.ipynb
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"cells": [
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".. _nb_algorithms_list:"
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},
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"source": [
".. csv-table:: Algorithms available in pymoo\n",
" :header: \"Algorithm\", \"Class\", \"Objective(s)\", \"Constraints\", \"Description\"\n",
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16 changes: 12 additions & 4 deletions docs/source/algorithms/moo/age.ipynb
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"cells": [
{
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"source": [
".. _nb_agemoea:"
]
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": []
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"outputs": [],
"source": [
"from pymoo.algorithms.moo.age import AGEMOEA\n",
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": []
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"outputs": [],
"source": [
"from pymoo.algorithms.moo.age import AGEMOEA\n",
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},
{
"cell_type": "raw",
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"source": [
".. autoclass:: pymoo.algorithms.moo.age.AGEMOEA\n",
" :noindex:"
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12 changes: 9 additions & 3 deletions docs/source/algorithms/moo/age2.ipynb
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"cells": [
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".. _nb_agemoea2:"
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from pymoo.algorithms.moo.age2 import AGEMOEA2\n",
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},
{
"cell_type": "raw",
"metadata": {},
"metadata": {
"raw_mimetype": "text/restructuredtext"
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"source": [
".. autoclass:: pymoo.algorithms.moo.age2.AGEMOEA2\n",
" :noindex:"
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16 changes: 12 additions & 4 deletions docs/source/algorithms/moo/ctaea.ipynb
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"cells": [
{
"cell_type": "raw",
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"source": [
".. _nb_ctaea:"
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"outputs": [],
"source": [
"from pymoo.algorithms.moo.ctaea import CTAEA\n",
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
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"outputs": [],
"source": [
"problem = get_problem(\"carside\")\n",
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},
{
"cell_type": "raw",
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"metadata": {
"raw_mimetype": "text/restructuredtext"
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"source": [
".. autoclass:: pymoo.algorithms.moo.ctaea.CTAEA\n",
" :noindex:"
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12 changes: 9 additions & 3 deletions docs/source/algorithms/moo/dnsga2.ipynb
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{
"cell_type": "raw",
"id": "1c4f9d85-e64f-4680-a937-1079d69d5c33",
"metadata": {},
"metadata": {
"raw_mimetype": "text/restructuredtext"
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"source": [
".. _nb_dnsga2:"
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{
"cell_type": "markdown",
"id": "f0a1dfd8-a241-4f73-b8db-3ed46ae7fd23",
"metadata": {},
"metadata": {
"tags": []
},
"source": [
"# D-NSGA-II: Dynamic Multi-Objective Optimization Using Modified NSGA-II"
]
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"cell_type": "code",
"execution_count": null,
"id": "4a786da2-8c26-406e-ad1e-c6f0159793c1",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from pymoo.algorithms.moo.dnsga2 import DNSGA2\n",
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16 changes: 12 additions & 4 deletions docs/source/algorithms/moo/kgb.ipynb
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"cells": [
{
"cell_type": "raw",
"metadata": {},
"metadata": {
"raw_mimetype": "text/restructuredtext"
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"source": [
".. _nb_kgb:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"tags": []
},
"source": [
"# KGB-DMOEA: Knowledge-Guided Bayesian Dynamic Multi-Objective Evolutionary Algorithm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"tags": []
},
"source": [
"KGB-DMOEA is a sophisticated evolutionary algorithm for dynamic multi-objective optimization problems (DMOPs). It employs a knowledge-guided Bayesian classification approach to adeptly navigate and adapt to changing Pareto-optimal solutions in dynamic environments. This algorithm utilizes past search experiences, distinguishing them as beneficial or non-beneficial, to effectively direct the search in new scenarios."
]
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},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"tags": []
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"source": [
"### Parameters \n",
"\n",
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8 changes: 6 additions & 2 deletions docs/source/algorithms/moo/moead.ipynb
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"cells": [
{
"cell_type": "raw",
"metadata": {},
"metadata": {
"raw_mimetype": "text/restructuredtext"
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"source": [
".. _nb_moead:"
]
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},
{
"cell_type": "raw",
"metadata": {},
"metadata": {
"raw_mimetype": "text/restructuredtext"
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"source": [
".. autoclass:: pymoo.algorithms.moo.moead.MOEAD\n",
" :noindex:"
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20 changes: 15 additions & 5 deletions docs/source/algorithms/moo/nsga2.ipynb
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"cells": [
{
"cell_type": "raw",
"metadata": {},
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. _nb_nsga2:"
]
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},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"tags": []
},
"source": [
"# NSGA-II: Non-dominated Sorting Genetic Algorithm"
]
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from pymoo.algorithms.moo.nsga2 import NSGA2\n",
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from pymoo.algorithms.moo.nsga2 import NSGA2\n",
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},
{
"cell_type": "raw",
"metadata": {},
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. autoclass:: pymoo.algorithms.moo.nsga2.NSGA2\n",
" :noindex:"
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