simple genetic algorithm.ipynb

simple-genetic-algorithm.ipynb

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  "metadata": {
    "colab": {
      "name": "simple genetic algorithm.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/vedraiyani/f99be52f7ec4009a3b992f980adb49f8/simple-genetic-algorithm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DRjsgWLqYon",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "import numpy as np\n",
        "\n",
        "def create_reference_solution(chromosome_length):\n",
        "\n",
        "    number_of_ones = int(chromosome_length / 2)\n",
        "\n",
        "    # Build an array with an equal mix of zero and ones\n",
        "    reference = np.zeros(chromosome_length)\n",
        "    reference[0: number_of_ones] = 1\n",
        "\n",
        "    # Shuffle the array to mix the zeros and ones\n",
        "    np.random.shuffle(reference)\n",
        "    \n",
        "    return reference"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWMktUBSqaEr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_starting_population(individuals, chromosome_length):\n",
        "    # Set up an initial array of all zeros\n",
        "    population = np.zeros((individuals, chromosome_length))\n",
        "    # Loop through each row (individual)\n",
        "    for i in range(individuals):\n",
        "        # Choose a random number of ones to create\n",
        "        ones = random.randint(0, chromosome_length)\n",
        "        # Change the required number of zeros to ones\n",
        "        population[i, 0:ones] = 1\n",
        "        # Sfuffle row\n",
        "        np.random.shuffle(population[i])\n",
        "    \n",
        "    return population"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jg2Z1HPKqlmy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def calculate_fitness(reference, population):\n",
        "    # Create an array of True/False compared to reference\n",
        "    identical_to_reference = population == reference\n",
        "    # Sum number of genes that are identical to the reference\n",
        "    fitness_scores = identical_to_reference.sum(axis=1)\n",
        "    \n",
        "    return fitness_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UuslHVGQqpwt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def select_individual_by_tournament(population, scores):\n",
        "    # Get population size\n",
        "    population_size = len(scores)\n",
        "    \n",
        "    # Pick individuals for tournament\n",
        "    fighter_1 = random.randint(0, population_size-1)\n",
        "    fighter_2 = random.randint(0, population_size-1)\n",
        "    \n",
        "    # Get fitness score for each\n",
        "    fighter_1_fitness = scores[fighter_1]\n",
        "    fighter_2_fitness = scores[fighter_2]\n",
        "    \n",
        "    # Identify undividual with highest fitness\n",
        "    # Fighter 1 will win if score are equal\n",
        "    if fighter_1_fitness >= fighter_2_fitness:\n",
        "        winner = fighter_1\n",
        "    else:\n",
        "        winner = fighter_2\n",
        "    \n",
        "    # Return the chromsome of the winner\n",
        "    return population[winner, :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibuoLGKEquKT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def breed_by_crossover(parent_1, parent_2):\n",
        "    # Get length of chromosome\n",
        "    chromosome_length = len(parent_1)\n",
        "    \n",
        "    # Pick crossover point, avoding ends of chromsome\n",
        "    crossover_point = random.randint(1,chromosome_length-1)\n",
        "    \n",
        "    # Create children. np.hstack joins two arrays\n",
        "    child_1 = np.hstack((parent_1[0:crossover_point],\n",
        "                        parent_2[crossover_point:]))\n",
        "    \n",
        "    child_2 = np.hstack((parent_2[0:crossover_point],\n",
        "                        parent_1[crossover_point:]))\n",
        "    \n",
        "    # Return children\n",
        "    return child_1, child_2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NjxukWrtqywC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def randomly_mutate_population(population, mutation_probability):\n",
        "    \n",
        "    # Apply random mutation\n",
        "        random_mutation_array = np.random.random(\n",
        "            size=(population.shape))\n",
        "        \n",
        "        random_mutation_boolean = \\\n",
        "            random_mutation_array <= mutation_probability\n",
        "\n",
        "        population[random_mutation_boolean] = \\\n",
        "        np.logical_not(population[random_mutation_boolean])\n",
        "        \n",
        "        # Return mutation population\n",
        "        return population"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fyQSYQAqI3Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "36aab8a3-97ff-4b4a-e0cf-a2fb2ebe63f6"
      },
      "source": [
        "# Set general parameters\n",
        "chromosome_length = 75\n",
        "population_size = 500\n",
        "maximum_generation = 200\n",
        "best_score_progress = [] # Tracks progress\n",
        "\n",
        "# Create reference solution \n",
        "# (this is used just to illustrate GAs)\n",
        "reference = create_reference_solution(chromosome_length)\n",
        "\n",
        "# Create starting population\n",
        "population = create_starting_population(population_size, chromosome_length)\n",
        "\n",
        "# Display best score in starting population\n",
        "scores = calculate_fitness(reference, population)\n",
        "best_score = np.max(scores)/chromosome_length * 100\n",
        "print ('Starting best score, percent target: %.1f' %best_score)\n",
        "\n",
        "# Add starting best score to progress tracker\n",
        "best_score_progress.append(best_score)\n",
        "\n",
        "# Now we'll go through the generations of genetic algorithm\n",
        "for generation in range(maximum_generation):\n",
        "    # Create an empty list for new population\n",
        "    new_population = []\n",
        "    \n",
        "    # Create new popualtion generating two children at a time\n",
        "    for i in range(int(population_size/2)):\n",
        "        parent_1 = select_individual_by_tournament(population, scores)\n",
        "        parent_2 = select_individual_by_tournament(population, scores)\n",
        "        child_1, child_2 = breed_by_crossover(parent_1, parent_2)\n",
        "        new_population.append(child_1)\n",
        "        new_population.append(child_2)\n",
        "    \n",
        "    # Replace the old population with the new one\n",
        "    population = np.array(new_population)\n",
        "    \n",
        "    # Score best solution, and add to tracker\n",
        "    scores = calculate_fitness(reference, population)\n",
        "    best_score = np.max(scores)/chromosome_length * 100\n",
        "    best_score_progress.append(best_score)\n",
        "\n",
        "# GA has completed required generation\n",
        "print ('End best score, percent target: %.1f' %best_score)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting best score, percent target: 65.3\n",
            "End best score, percent target: 100.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1pBD7M8qPVX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "66efb063-7a44-4465-baa3-8f1af54bfe2b"
      },
      "source": [
        "# Plot progress\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.plot(best_score_progress)\n",
        "plt.xlabel('Generation')\n",
        "plt.ylabel('Best score (% target)')\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}