5_SBSGA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyMSOO.MFEA.model import SBSGA\n",
    "from pyMSOO.utils.Crossover import *\n",
    "from pyMSOO.utils.Mutation import *\n",
    "from pyMSOO.utils.Selection import *\n",
    "from pyMSOO.utils.DimensionAwareStrategy import *\n",
    "from pyMSOO.MFEA.benchmark.continous import *\n",
    "from pyMSOO.utils.MultiRun.RunMultiTime import * \n",
    "from pyMSOO.utils.MultiRun.RunMultiBenchmark import * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tasks, IndClass = CEC17_benchmark.get_2tasks_benchmark(1)\n",
    "# tasks, IndClass = WCCI22_benchmark.get_complex_benchmark(1)\n",
    "tasks, IndClass = CEC17_benchmark.get_10tasks_benchmark()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored on calling ctypes callback function: <function ExecutionEngine._raw_object_cache_notify at 0x00000237AED459D0>\n",
      "Traceback (most recent call last):\n",
      "  File \"f:\\Anaconda\\Anaconda\\envs\\MFEA\\lib\\site-packages\\llvmlite\\binding\\executionengine.py\", line 171, in _raw_object_cache_notify\n",
      "    def _raw_object_cache_notify(self, data):\n",
      "KeyboardInterrupt: \n"
     ]
    }
   ],
   "source": [
    "baseModel = SBSGA.model()\n",
    "baseModel.compile(\n",
    "    IndClass= IndClass,\n",
    "    tasks= tasks,\n",
    "    # crossover = KL_SBXCrossover(nc= 2, k= 100, conf_thres= 1),\n",
    "    crossover= SBX_Crossover(nc = 2),\n",
    "    mutation= PolynomialMutation(nm = 5),\n",
    "    selection= ElitismSelection(),\n",
    "    dimension_strategy=DaS_strategy(eta= 3)\n",
    ")\n",
    "solve = baseModel.fit(\n",
    "    nb_generations = 1000, rmp = 0.5, nb_inds_each_task= 100, \n",
    "    bound_pop= [0, 1], evaluate_initial_skillFactor= True\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MUltitime Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseModel = MultiBenchmark(model= SBSGA, ls_benchmark=[tasks], ls_IndClass=[IndClass], name_benchmark = ['CEC17'])\n",
    "baseModel.compile(\n",
    "    IndClass= IndClass,\n",
    "    tasks= tasks,\n",
    "    # crossover = KL_SBXCrossover(nc= 2, k= 100, conf_thres= 1),\n",
    "    crossover= SBX_Crossover(nc = 2),\n",
    "    mutation= PolynomialMutation(nm = 5),\n",
    "    selection= ElitismSelection(),\n",
    "    dimension_strategy= NoDaS(),\n",
    ")\n",
    "solve = baseModel.fit(\n",
    "    nb_generations = 1000,nb_inds_each_task= 100, \n",
    "    bound_pop= [0, 1], evaluate_initial_skillFactor= True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed: 0 -- Time: 01m 37.31s  100 % [====================>]  Cost: 1.04E-06  3.25E-07  1.60E-08  0.00E+00  1.45E-04  1.63E-04  7.47E-03  6.38E-04  1.84E-08  2.28E+01  ,  \n",
      "END!\n",
      "Seed: 1 -- Time: 01m 29.96s  100 % [====================>]  Cost: 2.42E-06  3.30E-07  1.44E-10  0.00E+00  3.28E-04  1.77E-04  1.63E-03  6.39E-04  2.45E-08  2.65E+01  ,  \n",
      "END!\n",
      "DONE!\n",
      "Saved\n"
     ]
    }
   ],
   "source": [
    "baseModel.run(\n",
    "    nb_run= 2,\n",
    "    save_path= './RESULTS/SBSGA'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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