ies8-hm3-sga-vs-cga-quadratic-function-rn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sGA best solution is: (0, [2.1425363466494343e-06, -1.1294865482890587e-05, 1.8933524600477164e-06, -2.240406121740247e-06, 4.1966261074938995e-06])\n",
      "The cGA best solution is: (0, array([ 3.82451169e-06, -2.73728394e-06, -1.58617882e-06, -1.44489082e-06,\n",
      "        7.99844600e-07]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "################################################################################\n",
    "## Imports\n",
    "\n",
    "import sys\n",
    "import math\n",
    "import numpy\n",
    "numpy.set_printoptions(threshold=sys.maxsize)\n",
    "import random\n",
    "from pyeasyga import pyeasyga\n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "################################################################################\n",
    "## Common code\n",
    "\n",
    "# Error\n",
    "ERR = 1e-10\n",
    "\n",
    "# Data\n",
    "data = [0] * 5\n",
    "\n",
    "# Define fitness function\n",
    "def fitness_function(individual, data=None):\n",
    "  sum = 0\n",
    "  for u in individual:\n",
    "    sum += (u ** 2)\n",
    "  return sum\n",
    "\n",
    "# Define the key field for sorting\n",
    "def get_key(obj):\n",
    "  return obj.fitness\n",
    "\n",
    "def round_up(n, decimals=10):\n",
    "  multiplier = 10 ** decimals\n",
    "  return math.floor(n * multiplier) / multiplier\n",
    "\n",
    "################################################################################\n",
    "## Simple Genetic Algorithm (sGA)\n",
    "\n",
    "# Initialize genetic algorithm\n",
    "sga = pyeasyga.GeneticAlgorithm(data, maximise_fitness=False)\n",
    "\n",
    "# Set fitness function\n",
    "sga.fitness_function = fitness_function\n",
    "\n",
    "# Covariance matrices\n",
    "sga_cm = {}\n",
    "\n",
    "# Create a new individual\n",
    "def create_individual(data):\n",
    "  # Set mutation range\n",
    "  mrange = len(data)\n",
    "  # Generate a random individual\n",
    "  individual = []\n",
    "  for d in data:\n",
    "    individual.append(random.uniform(d - mrange, d + mrange))\n",
    "  # Return a new individual\n",
    "  return individual\n",
    "sga.create_individual = create_individual\n",
    "\n",
    "# Fix mutate function\n",
    "def mutate_function(individual):\n",
    "  # Set mutation range\n",
    "  mrange = max(individual)\n",
    "  mutate_index = random.randrange(len(individual))\n",
    "  d = individual[mutate_index]\n",
    "  individual[mutate_index] = random.uniform(d - mrange, d + mrange)\n",
    "sga.mutate_function = mutate_function\n",
    "\n",
    "# Fix population fitness calculation\n",
    "def calculate_population_fitness(self):\n",
    "  for individual in self.current_generation:\n",
    "    individual.fitness = self.fitness_function(individual.genes, self.seed_data)\n",
    "    individual.fitness = round_up(individual.fitness)\n",
    "    individual.fitness = 0 if ERR >= individual.fitness else individual.fitness\n",
    "sga.calculate_population_fitness = calculate_population_fitness\n",
    "\n",
    "# Set initial population generation function (fix rank population call)\n",
    "def create_first_generation(self):\n",
    "  self.create_initial_population()\n",
    "  self.calculate_population_fitness(self)\n",
    "  self.rank_population()\n",
    "sga.create_first_generation = create_first_generation\n",
    "\n",
    "# Set next population generation function (fix rank population call)\n",
    "def create_next_generation(self):\n",
    "  self.create_new_population()\n",
    "  self.calculate_population_fitness(self)\n",
    "  self.rank_population()\n",
    "sga.create_next_generation = create_next_generation\n",
    "\n",
    "# Set evolution function\n",
    "def run(self):\n",
    "  # Initialize seed data with random values\n",
    "  model_size = len(self.seed_data)\n",
    "  for i in range(model_size):\n",
    "    self.seed_data[i] = random.uniform(0, model_size)\n",
    "  # Run evolution\n",
    "  self.create_first_generation(self)\n",
    "  # Initial Covariance Matrix\n",
    "  arrs = [numpy.transpose(i.genes) for i in self.current_generation]\n",
    "  sga_cm['icm'] = numpy.cov(arrs)\n",
    "  for i in range(1, self.generations):\n",
    "    self.create_next_generation(self)\n",
    "    # Intermediary\n",
    "    if i == int(self.generations / 2):\n",
    "      # Intermediary Covariance Matrix\n",
    "      arrs = [numpy.transpose(i.genes) for i in self.current_generation]\n",
    "      sga_cm['tcm'] = numpy.cov(arrs)\n",
    "  # Final Covariance Matrix\n",
    "  arrs = [numpy.transpose(i.genes) for i in self.current_generation]\n",
    "  sga_cm['fcm'] = numpy.cov(arrs)\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",
    "# Initialize genetic algorithm\n",
    "cga = pyeasyga.GeneticAlgorithm(data, maximise_fitness=False)\n",
    "\n",
    "# Set fitness function\n",
    "cga.fitness_function = fitness_function\n",
    "\n",
    "# Covariance matrices\n",
    "cga_cm = {}\n",
    "\n",
    "# Generate probability vector\n",
    "def generate_prob(model_size):\n",
    "  prob = []\n",
    "  std_stdev = 1\n",
    "  for i in range(model_size):\n",
    "    bound = model_size + std_stdev\n",
    "    mean = random.uniform(-bound, bound)\n",
    "    pair = (mean, std_stdev)\n",
    "    prob.append(pair)\n",
    "  return prob\n",
    "\n",
    "# Update probability vector\n",
    "def update_prob(elite, prob):\n",
    "  # Update probability vector with the best results\n",
    "  for i in range(len(prob)):\n",
    "    # Mean and standard deviation of the ith element\n",
    "    aux = []\n",
    "    for item in elite:\n",
    "      mean = item.genes[i]\n",
    "      aux.append(mean)\n",
    "    # Update mean and stdev\n",
    "    prob[i] = numpy.mean(aux), numpy.std(aux, ddof=1)\n",
    "\n",
    "# Create a new individual\n",
    "def create_individual(prob):\n",
    "  individual = []\n",
    "  for p in prob:\n",
    "    mean, stdev = p\n",
    "    value = random.uniform(mean - stdev, mean + stdev)\n",
    "    individual.append(value)\n",
    "  mean = numpy.mean(individual)\n",
    "  stdev = numpy.std(individual, ddof=1)\n",
    "  individual = numpy.random.normal(mean, stdev, len(individual))\n",
    "  # Return a new individual\n",
    "  return pyeasyga.Chromosome(individual)\n",
    "cga.create_individual = create_individual\n",
    "\n",
    "icm_pop = None\n",
    "tcm_pop = None\n",
    "fcm_pop = None\n",
    "\n",
    "# Set evolution function\n",
    "def run(self):\n",
    "  # Initialize the max number of individuals in a offspring\n",
    "  offspring_max = self.population_size\n",
    "  # Initialize best solution\n",
    "  best = None\n",
    "  # Initialize best individuals population\n",
    "  k = int(self.population_size / 2)\n",
    "  population = []\n",
    "  arrs = []\n",
    "  # Initialize probability vector\n",
    "  prob = generate_prob(len(self.seed_data))\n",
    "  # Run `i` generations\n",
    "  for i in range(self.generations):\n",
    "    # Create individuals\n",
    "    for _ in range(offspring_max):\n",
    "      downward = self.create_individual(prob)\n",
    "      downward.fitness = round_up(self.fitness_function(downward.genes))\n",
    "      downward.fitness = 0 if ERR >= downward.fitness else downward.fitness\n",
    "      population.append(downward)\n",
    "    # Update best individuals population\n",
    "    population.sort(key=get_key)\n",
    "    population = population[:self.population_size]\n",
    "    elite = population[:k]\n",
    "    best = population[0]\n",
    "    # Initial population\n",
    "    if i == 0:\n",
    "      # Initial Covariance Matrix\n",
    "      arrs = [numpy.transpose(i.genes) for i in population]\n",
    "      cga_cm['icm'] = numpy.cov(arrs)\n",
    "    # Intermediary\n",
    "    if i == int(self.generations / 2):\n",
    "      # Intermediary Covariance Matrix\n",
    "      arrs = [numpy.transpose(i.genes) for i in population]\n",
    "      cga_cm['tcm'] = numpy.cov(arrs)\n",
    "    # Update the probability vector based on the success of each bit\n",
    "    update_prob(elite, prob)\n",
    "  # Add final solution\n",
    "  self.current_generation.append(best)\n",
    "  # Update best individuals population\n",
    "  population.sort(key=get_key)\n",
    "  population = population[:self.population_size]\n",
    "  # Final Covariance Matrix\n",
    "  arrs = [numpy.transpose(i.genes) for i in population]\n",
    "  cga_cm['fcm'] = numpy.cov(arrs)\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",
    "## Heat map\n",
    "\n",
    "def fmt(x, pos):\n",
    "  return r'{:.2f}'.format(x)\n",
    "\n",
    "# sGA\n",
    "\n",
    "hmmin, hmmax = -10, 10\n",
    "\n",
    "fig, axis = plt.subplots(figsize=(13, 10))\n",
    "fig.suptitle('sGA\\nCovariance Matrix of Initial Population',\n",
    "  fontsize=14, fontweight='bold', y=0.94)\n",
    "heatmap = axis.pcolor(sga_cm['icm'], vmin=hmmin, vmax=hmmax, cmap=plt.cm.Blues)\n",
    "plt.colorbar(heatmap, format=ticker.FuncFormatter(fmt))\n",
    "plt.show()\n",
    "\n",
    "fig, axis = plt.subplots(figsize=(13, 10))\n",
    "fig.suptitle('sGA\\nCovariance Matrix of Intermediary Population',\n",
    "  fontsize=14, fontweight='bold', y=0.94)\n",
    "heatmap = axis.pcolor(sga_cm['tcm'], vmin=hmmin, vmax=hmmax, cmap=plt.cm.Blues)\n",
    "plt.colorbar(heatmap, format=ticker.FuncFormatter(fmt))\n",
    "plt.show()\n",
    "\n",
    "fig, axis = plt.subplots(figsize=(13, 10))\n",
    "fig.suptitle('sGA\\nCovariance Matrix of Final Population',\n",
    "  fontsize=14, fontweight='bold', y=0.94)\n",
    "heatmap = axis.pcolor(sga_cm['fcm'], vmin=hmmin, vmax=hmmax, cmap=plt.cm.Blues)\n",
    "plt.colorbar(heatmap, format=ticker.FuncFormatter(fmt))\n",
    "plt.show()\n",
    "\n",
    "# cGA\n",
    "\n",
    "fig, axis = plt.subplots(figsize=(13, 10))\n",
    "fig.suptitle('cGA\\nCovariance Matrix of Initial Population',\n",
    "  fontsize=14, fontweight='bold', y=0.94)\n",
    "heatmap = axis.pcolor(cga_cm['icm'], vmin=hmmin, vmax=hmmax, cmap=plt.cm.Reds)\n",
    "plt.colorbar(heatmap, format=ticker.FuncFormatter(fmt))\n",
    "plt.show()\n",
    "\n",
    "fig, axis = plt.subplots(figsize=(13, 10))\n",
    "fig.suptitle('cGA\\nCovariance Matrix of Intermediary Population',\n",
    "  fontsize=14, fontweight='bold', y=0.94)\n",
    "heatmap = axis.pcolor(cga_cm['tcm'], vmin=hmmin, vmax=hmmax, cmap=plt.cm.Reds)\n",
    "plt.colorbar(heatmap, format=ticker.FuncFormatter(fmt))\n",
    "plt.show()\n",
    "\n",
    "fig, axis = plt.subplots(figsize=(13, 10))\n",
    "fig.suptitle('cGA\\nCovariance Matrix of Final Population',\n",
    "  fontsize=14, fontweight='bold', y=0.94)\n",
    "heatmap = axis.pcolor(cga_cm['fcm'], vmin=hmmin, vmax=hmmax, cmap=plt.cm.Reds)\n",
    "plt.colorbar(heatmap, format=ticker.FuncFormatter(fmt))\n",
    "plt.show()\n",
    "\n",
    "################################################################################"
   ]
  }
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