ies3-sga-vs-cga-one-max-problem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sGA best solution is: (5, [1, 1, 1, 1, 1])\n",
      "The cGA best solution is: (5, [1, 1, 1, 1, 1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "## Imports\n",
    "\n",
    "from random import random\n",
    "from pyeasyga import pyeasyga\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "################################################################################\n",
    "## Common code\n",
    "\n",
    "# Data\n",
    "data = [0] * 5\n",
    "\n",
    "# Define fitness function\n",
    "def fitness_function(individual, data=None):\n",
    "  return sum(individual)\n",
    "\n",
    "################################################################################\n",
    "## Simple Genetic Algorithm (sGA)\n",
    "\n",
    "# Best score progress\n",
    "progress_sga = []\n",
    "\n",
    "# Initialize genetic algorithm\n",
    "sga = pyeasyga.GeneticAlgorithm(data)\n",
    "\n",
    "# Set fitness function\n",
    "sga.fitness_function = fitness_function\n",
    "\n",
    "# Set evolution function\n",
    "def run(self):\n",
    "  self.create_first_generation()\n",
    "  for _ in range(1, self.generations):\n",
    "    self.create_next_generation()\n",
    "    fitness, _ = sga.best_individual()\n",
    "    progress_sga.append(fitness)\n",
    "sga.run = run\n",
    "\n",
    "# Run sGA\n",
    "sga.run(sga)\n",
    "# Get best individual\n",
    "result = sga.best_individual()\n",
    "# Print result\n",
    "print('The sGA best solution is: {}'.format(result))\n",
    "\n",
    "################################################################################\n",
    "## Compact Genetic Algorithm (cGA)\n",
    "\n",
    "# Best score progress\n",
    "progress_cga = []\n",
    "\n",
    "# Initialize genetic algorithm\n",
    "cga = pyeasyga.GeneticAlgorithm(data)\n",
    "\n",
    "# Update probability vector\n",
    "def update_prob(winner, loser, prob, popsize):\n",
    "  for i in range(0, len(prob)):\n",
    "    if winner[i] != loser[i]:\n",
    "      if winner[i] == 1:\n",
    "        prob[i] += 1.0 / float(popsize)\n",
    "      else:\n",
    "        prob[i] -= 1.0 / float(popsize)\n",
    "\n",
    "# Create a new individual\n",
    "def create_individual(prob):\n",
    "  individual = []\n",
    "  for p in prob:\n",
    "    if random() < p:\n",
    "      individual.append(1)\n",
    "    else:\n",
    "      individual.append(0)\n",
    "  return pyeasyga.Chromosome(individual)\n",
    "cga.create_individual = create_individual\n",
    "\n",
    "# Make competition between two individuals\n",
    "def compete(a, b):\n",
    "  if a.fitness > b.fitness:\n",
    "    return a, b\n",
    "  else:\n",
    "    return b, a\n",
    "\n",
    "# Set fitness function\n",
    "cga.fitness_function = fitness_function\n",
    "\n",
    "# Set evolution function\n",
    "def run(self):\n",
    "  # Initialize probability vector\n",
    "  prob = [0.5] * len(self.seed_data)\n",
    "  # Initialize best solution\n",
    "  best = None\n",
    "  # Run `i` generations\n",
    "  for _ in range(0, self.generations):\n",
    "    # Create individuals\n",
    "    a = self.create_individual(prob)\n",
    "    b = self.create_individual(prob)\n",
    "    # Calculate fitness for each individual\n",
    "    a.fitness = self.fitness_function(a.genes)\n",
    "    b.fitness = self.fitness_function(b.genes)\n",
    "    # Get the best and worst individual\n",
    "    winner, loser = compete(a, b)\n",
    "    # Update best solution\n",
    "    if best:\n",
    "      if winner.fitness > best.fitness:\n",
    "        best = winner\n",
    "    else:\n",
    "      best = winner\n",
    "    # Update the probability vector based on the success of each bit\n",
    "    update_prob(winner.genes, loser.genes, prob, self.population_size)\n",
    "    progress_cga.append(best.fitness)\n",
    "  # Add final solution\n",
    "  self.current_generation.append(best)\n",
    "cga.run = run\n",
    "\n",
    "# Run evolution\n",
    "cga.run(cga)\n",
    "# Get best individual\n",
    "result = cga.best_individual()\n",
    "# Print result\n",
    "print('The cGA best solution is: {}'.format(result))\n",
    "\n",
    "################################################################################\n",
    "## Plot comparison chart\n",
    "\n",
    "line_sga, = plt.plot(progress_sga, label='sGA')\n",
    "line_cga, = plt.plot(progress_cga, label='cGA')\n",
    "plt.legend([line_sga, line_cga], ['sGA', 'cGA'])\n",
    "plt.xlabel('Generation')\n",
    "plt.ylabel('Fitness')\n",
    "plt.show()\n",
    "\n",
    "################################################################################"
   ]
  }
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