ALGORITMO_GENETICO.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "< GRUPO 7 >\n",
        "\n",
        "PILAR ARIAS, ÁLVARO GARCÍA CID, YEDRA MARTÍN, AMAYA SARASA"
      ],
      "metadata": {
        "id": "bqM6hltHrW_7"
      },
      "id": "bqM6hltHrW_7"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Instalamos la librería Deap"
      ],
      "metadata": {
        "id": "W5LzhuawrnTV"
      },
      "id": "W5LzhuawrnTV"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install deap"
      ],
      "metadata": {
        "id": "SrXm1Xi5r60l",
        "outputId": "ed757c76-ff93-4f19-8e36-9da19cf48b6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "SrXm1Xi5r60l",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: deap in /usr/local/lib/python3.8/dist-packages (1.3.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from deap) (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install matplotlib"
      ],
      "metadata": {
        "id": "FPUK3ZCir9wv",
        "outputId": "d3a114fc-c578-4599-f8c2-2a57da8f3470",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "FPUK3ZCir9wv",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (3.2.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (1.21.6)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install numpy"
      ],
      "metadata": {
        "id": "9Kqo6jRPr9hm",
        "outputId": "ee2aabe4-98df-4741-e292-cb654ec4f78c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "9Kqo6jRPr9hm",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scipy"
      ],
      "metadata": {
        "id": "4URUag6-r9X7",
        "outputId": "134a01c2-4183-4ed6-b506-a375bd0fd42e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "4URUag6-r9X7",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (1.7.3)\n",
            "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in /usr/local/lib/python3.8/dist-packages (from scipy) (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scoop"
      ],
      "metadata": {
        "id": "YF4OzDEUr9Bz",
        "outputId": "17965576-6dd8-45d6-a7df-f9e74ed80c20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "YF4OzDEUr9Bz",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scoop in /usr/local/lib/python3.8/dist-packages (0.7.2.0)\n",
            "Requirement already satisfied: pyzmq>=13.1.0 in /usr/local/lib/python3.8/dist-packages (from scoop) (23.2.1)\n",
            "Requirement already satisfied: greenlet>=0.3.4 in /usr/local/lib/python3.8/dist-packages (from scoop) (2.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import math\n",
        "import numpy as np\n",
        "from deap import base\n",
        "from deap import creator\n",
        "from deap import tools\n",
        "from deap import algorithms\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "1PtCVirfsyoR"
      },
      "id": "1PtCVirfsyoR",
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importamos la librería\n",
        "import random\n",
        "# Fijamos la semilla. Si no cambiamos la semilla, \n",
        "# la función generará los mismos número aleatorios\n",
        "random.seed(0)\n",
        "# Generamos los números pseudo-aleatorios en 2 list comprenhension\n",
        "# para encapsular todos los datos en las listas x e y\n",
        "x = [random.uniform(-100, 100) for i in range(10)]\n",
        "y = [random.uniform(-100, 100) for i in range(10)]\n",
        "print('x= ', x)\n",
        "print('y= ', y)"
      ],
      "metadata": {
        "id": "PvyIDU9Ks52C",
        "outputId": "c62cbbaa-b178-4c1b-a4b4-593c76ce612d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "PvyIDU9Ks52C",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x=  [68.88437030500961, 51.590880588060486, -15.885683833830996, -48.21664994140733, 2.254944273721705, -19.01317250991714, 56.75971780695451, -39.33745478421451, -4.680609169528836, 16.676407891006235]\n",
            "y=  [81.62257703906704, 0.9373711634780477, -43.63243112005924, 51.16084083144477, 23.67379933506632, -49.898731727511894, 81.94925119364802, 96.55709520753061, 62.04344719931791, 80.43319008791653]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importamos las librerías necesarias\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from matplotlib import cm"
      ],
      "metadata": {
        "id": "nI-rzigKtBBm"
      },
      "id": "nI-rzigKtBBm",
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Problema por resolver:"
      ],
      "metadata": {
        "id": "SznfbSERcymz"
      },
      "id": "SznfbSERcymz"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se desean Encontrar los **máximos** de la función \"picos\"\n",
        "\n",
        "$$z = f(x,y) = 3(1-x)^2e^{-(x^2)-(y+1)^2}-10(x/5-x^3-y^5)e^{-x^2-y^2}-1/3e^{-(x+1)^2-y^2}$$\n",
        "\n",
        "En el dominio $$\\{x,y\\} \\in [-100,100]$$"
      ],
      "metadata": {
        "id": "9mJ56kse_3zU"
      },
      "id": "9mJ56kse_3zU"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definimos la **función objetivo**\n",
        "\n",
        "Para poder introducir correctamente esta función, se ha partido en diferentes segmentos para su correcta ejecución."
      ],
      "metadata": {
        "id": "BOUW8MrFc67R"
      },
      "id": "BOUW8MrFc67R"
    },
    {
      "cell_type": "code",
      "source": [
        "# Definimos la función objetivo\n",
        "def funcion_prueba(x):\n",
        "    # x es x[0] e y es x[1]\n",
        "    res1 = (1-x[0])**2\n",
        "    res2 = np.exp(-(x[0]**2+(x[1]+1)**2))\n",
        "    res3 = x[0]/5-x[0]**3-x[1]**5\n",
        "    res4 = np.exp(-(x[0]**2)-(x[1]**2))\n",
        "    res5 = (1/3)*np.exp(-((x[0]+1)**2)-x[1]**2)\n",
        "    res = 3* res1 * res2 - 10 * res3 * res4 - res5\n",
        "    return res"
      ],
      "metadata": {
        "id": "8HgZMZFstByz"
      },
      "id": "8HgZMZFstByz",
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generamos vectores para comenzar la exploración del máximo de la función.\n",
        "\n",
        "Intentaremos ajustar los parámetros lo máximo posible entorno a los valores que sabemos contendrán los máximos de la función.\n",
        "\n",
        "Para ello usaremos la función ``linspace`` que crea secuencias de valores espaciados uniformemente dentro de un intervalo definido. Esencialmente, especifica un punto de inicio y un punto final de un intervalo, y luego especifica el número total de puntos de interrupción que desea dentro de ese intervalo (incluidos los puntos inicial y final).\n",
        "\n",
        "En este caso creamos una matriz de 1 000 números espaciados uniformemente en el intervalo [-5, 5]."
      ],
      "metadata": {
        "id": "mSQocxnGugcU"
      },
      "id": "mSQocxnGugcU"
    },
    {
      "cell_type": "code",
      "source": [
        "# Generamos vectores que barran todo el dominio\n",
        "x = np.linspace(-5, 5, 1000)\n",
        "y = np.linspace(-5, 5, 1000)\n",
        "# print('x= ', x)\n",
        "# print('y= ', y)"
      ],
      "metadata": {
        "id": "ui2WaUNVtGAh"
      },
      "id": "ui2WaUNVtGAh",
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "La función **numpy.meshgrid** devuelve dos matrices bidimensionales que representan las coordenadas X e Y de todos los puntos. "
      ],
      "metadata": {
        "id": "GOmcQkSICjXm"
      },
      "id": "GOmcQkSICjXm"
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluamos todos los valores de las variables\n",
        "x_ax, y_ax = np.meshgrid(x, y)\n",
        "print(\"x_ax = \")\n",
        "print(x_ax)\n",
        "print(\"y_ax = \")\n",
        "print(y_ax)       \n"
      ],
      "metadata": {
        "id": "KBByeSwCtJsI",
        "outputId": "3425aeaa-17a0-4aa0-9092-1eef123e5a6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "KBByeSwCtJsI",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_ax = \n",
            "[[-5.         -4.98998999 -4.97997998 ...  4.97997998  4.98998999\n",
            "   5.        ]\n",
            " [-5.         -4.98998999 -4.97997998 ...  4.97997998  4.98998999\n",
            "   5.        ]\n",
            " [-5.         -4.98998999 -4.97997998 ...  4.97997998  4.98998999\n",
            "   5.        ]\n",
            " ...\n",
            " [-5.         -4.98998999 -4.97997998 ...  4.97997998  4.98998999\n",
            "   5.        ]\n",
            " [-5.         -4.98998999 -4.97997998 ...  4.97997998  4.98998999\n",
            "   5.        ]\n",
            " [-5.         -4.98998999 -4.97997998 ...  4.97997998  4.98998999\n",
            "   5.        ]]\n",
            "y_ax = \n",
            "[[-5.         -5.         -5.         ... -5.         -5.\n",
            "  -5.        ]\n",
            " [-4.98998999 -4.98998999 -4.98998999 ... -4.98998999 -4.98998999\n",
            "  -4.98998999]\n",
            " [-4.97997998 -4.97997998 -4.97997998 ... -4.97997998 -4.97997998\n",
            "  -4.97997998]\n",
            " ...\n",
            " [ 4.97997998  4.97997998  4.97997998 ...  4.97997998  4.97997998\n",
            "   4.97997998]\n",
            " [ 4.98998999  4.98998999  4.98998999 ...  4.98998999  4.98998999\n",
            "   4.98998999]\n",
            " [ 5.          5.          5.         ...  5.          5.\n",
            "   5.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**numpy.c_** Traslada objetos de división a concatenación a lo largo del segundo eje.\n",
        "\n",
        "**numpy.ravel** Devuelve una matriz 1-D aplanada contigua que contiene los elementos de la entrada. "
      ],
      "metadata": {
        "id": "Yp5KDxkzD4BY"
      },
      "id": "Yp5KDxkzD4BY"
    },
    {
      "cell_type": "code",
      "source": [
        "vals = np.c_[x_ax.ravel(), y_ax.ravel()]\n",
        "vals.shape"
      ],
      "metadata": {
        "id": "frcId09LDqhP",
        "outputId": "8301173a-2373-489f-d7a0-be402f1e73f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "frcId09LDqhP",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hacemos que los valores tomen una forma distinta"
      ],
      "metadata": {
        "id": "Hha0wYlcD7pS"
      },
      "id": "Hha0wYlcD7pS"
    },
    {
      "cell_type": "code",
      "source": [
        "fx = np.reshape([funcion_prueba(val) for val in vals],(1000, 1000))\n",
        "fx.shape"
      ],
      "metadata": {
        "id": "xN-VR9E7D9Wx",
        "outputId": "195ceac9-4625-4a6f-bdf2-fcd6d4c568ab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "xN-VR9E7D9Wx",
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 1000)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Representamos la función para orientarnos sobre dónde estará ese máximo global que se está buscando.\n",
        "\n",
        "**matplotlib.pyplot.figure** Crea una nueva gráfica o active una función existente."
      ],
      "metadata": {
        "id": "wBBCC0QBusFp"
      },
      "id": "wBBCC0QBusFp"
    },
    {
      "cell_type": "code",
      "source": [
        "# Representamos los resultados obtenidos\n",
        "figure_3d = plt.figure(figsize=(8,6)) # figsize determina el tamaño en pulgadas de la imagen\n",
        "ax = figure_3d.gca(projection=\"3d\")\n",
        "ax.plot_surface(x_ax, y_ax, fx, cmap=cm.coolwarm)\n",
        "ax.set_xlabel(\"x\")\n",
        "ax.set_ylabel(\"y\")\n",
        "ax.set_zlabel(\"f(x,y)\")"
      ],
      "metadata": {
        "id": "Y0IIzWmBtORg",
        "outputId": "d5971879-f324-4e8e-c6b2-eb6c7001e1f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "id": "Y0IIzWmBtORg",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'f(x,y)')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#A continuación\n",
        "\n",
        "1. Definiremos el problema y generaremos la población inicial\n",
        "\n",
        "2. Veremos la función objetivo y los operadores genéticos\n",
        "\n",
        "3. Como configurar el algoritmo genético y la representación de los resultados del algoritmo.\n"
      ],
      "metadata": {
        "id": "-nZz2Ru9d1qN"
      },
      "id": "-nZz2Ru9d1qN"
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Creación del problema "
      ],
      "metadata": {
        "id": "1kcwTU_OJFhc"
      },
      "id": "1kcwTU_OJFhc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importamos las librerías necesarias"
      ],
      "metadata": {
        "id": "m6Ob6H60flf1"
      },
      "id": "m6Ob6H60flf1"
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "from deap import base\n",
        "from deap import creator\n",
        "from deap import tools"
      ],
      "metadata": {
        "id": "5J0t2bUffh16"
      },
      "id": "5J0t2bUffh16",
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Creamos una nueva clase llamada `name=FitnessMax`, que hereda de `base=base.Fitness` y que tiene un atributo llamado `attribute=weights=1.0` en nuestro caso puesto que nuestro único objetivo es la maximización. \n"
      ],
      "metadata": {
        "id": "7PJ41n0vJShs"
      },
      "id": "7PJ41n0vJShs"
    },
    {
      "cell_type": "code",
      "source": [
        "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))"
      ],
      "metadata": {
        "id": "8WPY9O64tV3M",
        "outputId": "2330223d-1178-48b4-ee67-06f26e5539c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "8WPY9O64tV3M",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/deap/creator.py:138: RuntimeWarning: A class named 'FitnessMax' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
            "  warnings.warn(\"A class named '{0}' has already been created and it \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Creación de la plantilla del individuo "
      ],
      "metadata": {
        "id": "sdI8zS9lJyGr"
      },
      "id": "sdI8zS9lJyGr"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creamos el **tipo de individuo** y sus atributos. \n",
        "\n",
        "Simplemente al pensar en los diferentes tipos de algoritmos evolutivos (GA, GP, ES, PSO, DE, …), notamos que es posible una variedad extremadamente grande de individuos, por lo tanto nosotros mismos crearemos un individuo que satisfaga nuestros requerimiento usando el creador e inicializándolas usando una Caja de herramientas.\n",
        "\n",
        "Para producir el primer individuo, necesitamos crear una clase `Individual` usando el creador, heredado de ``list`` y tendrá el  atributo `fitness`"
      ],
      "metadata": {
        "id": "vmAXmQ4wJ7rC"
      },
      "id": "vmAXmQ4wJ7rC"
    },
    {
      "cell_type": "code",
      "source": [
        "creator.create(\"Individual\", list, fitness=creator.FitnessMax)"
      ],
      "metadata": {
        "id": "VtM1wdO6tZIX",
        "outputId": "f2c6c991-7bf5-4b68-c51a-7b6d66487297",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "VtM1wdO6tZIX",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/deap/creator.py:138: RuntimeWarning: A class named 'Individual' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
            "  warnings.warn(\"A class named '{0}' has already been created and it \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creación de individuos aleatorios y población inicial"
      ],
      "metadata": {
        "id": "55zxuZQtKHbA"
      },
      "id": "55zxuZQtKHbA"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usaremos el `Toolbox` que contendrá, mediante \n",
        "**registro**, un conjunto de funciones utilizadas por el algoritmo durante \n",
        "su ejecución."
      ],
      "metadata": {
        "id": "F32XZHXQKEv0"
      },
      "id": "F32XZHXQKEv0"
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox = base.Toolbox()"
      ],
      "metadata": {
        "id": "_P-erIC0tbn0"
      },
      "id": "_P-erIC0tbn0",
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "En el `Toolbox` registraremos funciones necesarias para ejecutar el AG."
      ],
      "metadata": {
        "id": "2e9Av3Y3KSEk"
      },
      "id": "2e9Av3Y3KSEk"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Crearemos **individuos aleatorios** usando la función`random.uniform` que nos permita generar un valor aleatorio para cada variable independiente (cada gen del comosoma), esto es, cada una de las posiciones de la lista con valores sujetos a las restricciones de nuestro problema (-100, 100)"
      ],
      "metadata": {
        "id": "v6ARvC7sKVcn"
      },
      "id": "v6ARvC7sKVcn"
    },
    {
      "cell_type": "code",
      "source": [
        "# Generación de genes\n",
        "toolbox.register(\"attr_uniform\", random.uniform, -100, 100)"
      ],
      "metadata": {
        "id": "N0NKusuwtfOF"
      },
      "id": "N0NKusuwtfOF",
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Probamos y vemos que generamos un número leatorio entre -100 y 100 distinto cada vez\n",
        "toolbox.attr_uniform()"
      ],
      "metadata": {
        "id": "-JrwQYVjtfsP",
        "outputId": "9e9f6ce8-7f38-4c5f-9b38-ffdd8ae43d20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "-JrwQYVjtfsP",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-37.970486136133474"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creamos el individuo completo llamando a la función que genera cada uno de los individuos tantas veces como variables independientes tengamos. "
      ],
      "metadata": {
        "id": "tvqw2x_DKZe2"
      },
      "id": "tvqw2x_DKZe2"
    },
    {
      "cell_type": "code",
      "source": [
        "# Generación de inviduos\n",
        "toolbox.register(\"individual\", \n",
        "                 tools.initRepeat, \n",
        "                 creator.Individual,\n",
        "                 toolbox.attr_uniform, 2) #2 = Número de veces que se llamará a la función; 2 cromosomas a crear"
      ],
      "metadata": {
        "id": "PKCbammrtqKZ"
      },
      "id": "PKCbammrtqKZ",
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ejemplo de la generación de un individuo aleatorio\n",
        "individuo = toolbox.individual()\n",
        "individuo"
      ],
      "metadata": {
        "id": "UHkFZcz_tsPS",
        "outputId": "1c7b9b2a-0819-4731-94d2-45224ab4dea8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "UHkFZcz_tsPS",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[45.96634965202574, 79.7676575935987]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Recordar que individuo es una lista que tiene un atributo fitness\n",
        "# donde se almacena la calidad del mismo, que al principio será\n",
        "# vacío, porque aun no ha sido evaluado\n",
        "individuo.fitness.values"
      ],
      "metadata": {
        "id": "aZYiMp45ttBc",
        "outputId": "e63fde0d-9a9b-4505-9f87-159baac94789",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "aZYiMp45ttBc",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "()"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Generación de la población"
      ],
      "metadata": {
        "id": "lZnfC4vvKm7R"
      },
      "id": "lZnfC4vvKm7R"
    },
    {
      "cell_type": "code",
      "source": [
        "# Generación de población\n",
        "toolbox.register(\"population\", tools.initRepeat, list,\n",
        "                 toolbox.individual, 100)"
      ],
      "metadata": {
        "id": "0ySNJ5m8tvN3"
      },
      "id": "0ySNJ5m8tvN3",
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poblacion = toolbox.population()\n",
        "print(poblacion)"
      ],
      "metadata": {
        "id": "sf_heb2rtyE0",
        "outputId": "38b5affd-1de2-492f-f7db-639b8b016bb5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "sf_heb2rtyE0",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[36.79678638308826, -5.571456909457325], [-79.85975838632683, -13.165632909243257], [22.177394688760316, 82.60221064757965], [93.32127355415176, -4.5980446894566], [73.061985554328, -47.90153792160812], [61.005565402604475, 9.739860767117861], [-97.19165996719622, 43.94093728079082], [-20.235291555146247, 64.96899542964661], [33.63064024637018, -99.77143613711435], [-1.284426706935065, 73.52055509855617], [-51.2178246225736, -34.95912745052199], [74.09424642173093, -61.78658169952189], [13.50214812413438, -52.27681427695596], [93.50805005802866, 60.63589385597402], [-10.406085712885925, -83.91083628949292], [-35.98907906549084, 1.5881285041147777], [86.56676484538136, -78.18843081377926], [10.253449218110248, 41.312281973377935], [9.488182265684756, 62.8933726582672], [8.056721394064795, 92.76770919476019], [20.63712559227659, 17.52341283508727], [-11.00219474489677, 19.257372316621257], [-23.019770805467914, 15.130202832977702], [-41.934099519448395, -62.121734289128774], [-62.654094348888975, 22.554635973721332], [31.331877797925756, -4.693801598123855], [-82.03512776088127, 51.52078439328736], [75.35407416455496, 84.67620318925611], [68.4920446280365, 79.6346242715758], [84.61648796403537, 8.119984989610884], [-21.740789953075023, 41.05667997088125], [-44.87317573757457, 62.32574170157571], [69.89719303727341, 79.00779348533504], [17.960236706231967, 89.95297464642411], [15.93900214912118, -9.887378673768964], [32.0490757244778, 99.25156787071455], [83.38824358949122, 58.66501682604485], [-83.52540236067051, 22.556621008142443], [-2.711159606166632, 26.029468082294557], [69.01551513430303, -51.39287558762875], [46.29784415816957, -76.5731413582964], [-55.9078926264343, 58.91659434211519], [-33.4927701560689, 63.18261930673191], [-79.87849595678077, -70.72830221753924], [39.53412803824776, -90.95318642687752], [14.773207357833385, 82.00320293980795], [6.839593652144799, 36.1178265124513], [-94.66064106755896, 26.999981982291658], [21.267683550843785, 15.190589606308151], [-21.75811813543463, -25.972011932962502], [96.10333012945372, -92.72159247770284], [-95.67269802899519, 92.20625604792224], [-63.005611720512334, -75.22096711511367], [-57.88469802267071, 60.14931807083619], [87.39383172891615, -95.44348486626832], [-14.876233606636575, -79.69995612516605], [-48.0160220414336, -55.83414573673653], [29.385143967064522, -29.94120652069354], [-63.93641969406243, 0.7273010419774408], [-92.12425858306152, -79.81575176220667], [97.6470297445002, -60.128841906587404], [-28.288939737679627, 46.319661245072126], [67.66531303868325, 83.69641239906628], [-66.11507878050647, 34.52811271461053], [93.30978060863663, -88.38981123470026], [35.24035685987565, 69.08491874032327], [-31.537491784283205, -49.86253214297767], [19.3582786938822, -11.53719326018421], [-65.03610310971177, -5.67491698074241], [-18.01892086848909, 13.822547904856037], [1.7200260125266311, -37.710799799958636], [-28.56966348194743, 67.5322348737958], [-49.813467035572586, 12.120043770704797], [-97.51273623413712, 48.31487548213272], [-32.81668910530789, -90.86070128631667], [-43.82336715633035, -51.97391843472921], [90.62586796555979, -29.55488769689852], [-42.42441702871999, -28.159760549250734], [89.38116713157822, 26.74957044985051], [24.215369123733453, 43.12387006029127], [-22.396552937498868, -17.116402344550536], [30.166572452669016, -99.69515562865597], [-61.53809175106484, -33.11966186749969], [-52.11680796280829, 27.479880225860057], [-24.270385935381114, 75.08467834260344], [13.63028418203838, -17.118720663271134], [-19.546584976184093, 40.36592478673509], [-16.354689341506784, 32.43917779476348], [-90.64406280864034, -10.929562056234033], [-48.15461531055545, -68.46268557553783], [5.51462603352293, -2.5468797861935855], [12.280985122885383, 51.09695345173651], [76.77503084974018, -1.0834659249426437], [-37.588350716625406, -6.621552929495294], [61.80917147207248, 75.00326629605422], [62.482986472751804, -62.3997411898344], [99.88407189106607, 26.61775198366007], [-83.30658996485414, 45.11087109226247], [97.36429604102563, -19.63663555749129], [35.703001048393645, -36.76457255573153]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "poblacion[0]"
      ],
      "metadata": {
        "id": "en4be_Aft0IR",
        "outputId": "7fdc607f-853a-4865-d0ba-8e4ac0eb0193",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "en4be_Aft0IR",
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[36.79678638308826, -5.571456909457325]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "poblacion[0].fitness.values"
      ],
      "metadata": {
        "id": "PSU4pD_Zt2E4",
        "outputId": "231b0d5b-e58b-4cab-f08b-8756fb2fca84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "PSU4pD_Zt2E4",
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "()"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Función objetivo y operadores genéticos"
      ],
      "metadata": {
        "id": "phtvEQGRQUh_"
      },
      "id": "phtvEQGRQUh_"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creamos nuestra **función objetivo** "
      ],
      "metadata": {
        "id": "fyWZJYJYQY3b"
      },
      "id": "fyWZJYJYQY3b"
    },
    {
      "cell_type": "code",
      "source": [
        "def funcion_objetivo(x):\n",
        "    \"\"\"\n",
        "    Función objetivo de nuestro problema\n",
        "    \"\"\"\n",
        "    for i in range(len(x)):\n",
        "        if x[i] > 100 or x[i] < -100:\n",
        "            return -1,\n",
        "    res1 = (1-x[0])**2\n",
        "    res2 = np.exp(-(x[0]**2+(x[1]+1)**2))\n",
        "    res3 = x[0]/5-x[0]**3-x[1]**5\n",
        "    res4 = np.exp(-(x[0]**2)-(x[1]**2))\n",
        "    res5 = (1/3)*np.exp(-((x[0]+1)**2)-x[1]**2)\n",
        "    res = 3* res1 * res2 - 10 * res3 * res4 - res5\n",
        "    return res,\n"
      ],
      "metadata": {
        "id": "gFLhinh0t6Zl"
      },
      "id": "gFLhinh0t6Zl",
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora, registramos la función de fitness"
      ],
      "metadata": {
        "id": "p4wfpQplQua3"
      },
      "id": "p4wfpQplQua3"
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox.register(\"evaluate\", funcion_objetivo)"
      ],
      "metadata": {
        "id": "6oAhyVoSt8qk"
      },
      "id": "6oAhyVoSt8qk",
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comprobamos el individuo generado"
      ],
      "metadata": {
        "id": "lugIckQDveBo"
      },
      "id": "lugIckQDveBo"
    },
    {
      "cell_type": "code",
      "source": [
        "individuo"
      ],
      "metadata": {
        "id": "G5H6fyWe7Z6o",
        "outputId": "b63d408e-25a0-4aa6-e047-92e9bb56c2de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "G5H6fyWe7Z6o",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[45.96634965202574, 79.7676575935987]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ejemplo de uso 1\n",
        "funcion_objetivo(individuo)"
      ],
      "metadata": {
        "id": "_mlAR7N_t9TV",
        "outputId": "de3937e0-9163-48f3-c06f-fb51282d54fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "_mlAR7N_t9TV",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0,)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ejemplo de uso 2\n",
        "toolbox.evaluate(individuo)"
      ],
      "metadata": {
        "id": "du25lcAht_QK",
        "outputId": "62bd3ab4-80ea-4bd8-ae69-741f191aa417",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "du25lcAht_QK",
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0,)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Distintos puntos de cruce"
      ],
      "metadata": {
        "id": "vHPSIfN2XwwA"
      },
      "id": "vHPSIfN2XwwA"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Crossover** es un operador genético utilizado para variar la programación de un cromosoma o cromosomas de una generación a la siguiente. \n",
        "\n",
        "Crossover es reproducción sexual. Se seleccionan al azar dos hilos del grupo de apareamiento para cruzarlos y producir una descendencia superior. \n",
        "\n",
        "Veremos 3 tipos distintos de cruces:\n",
        "\n",
        "1. **Punto de cruce único**: Se selecciona un punto de cruce en la cadena del organismo principal. Todos los datos más allá de ese punto en la cadena del organismo se intercambian entre los dos organismos principales. Las cadenas se caracterizan por el sesgo posicional.\n",
        "\n",
        "2. **Crossover de Dos Puntos**: Se eligen dos puntos aleatorios en los cromosomas individuales (cadenas) y el material genético se intercambia en estos puntos.\n",
        "\n",
        "3. **Cruce uniforme**: cada gen (bit) se selecciona aleatoriamente de uno de los genes correspondientes de los cromosomas originales.\n"
      ],
      "metadata": {
        "id": "I74fWmqcbO3M"
      },
      "id": "I74fWmqcbO3M"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Un único punto de cruce "
      ],
      "metadata": {
        "id": "c8Z-gU_fm18_"
      },
      "id": "c8Z-gU_fm18_"
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**MUTACIÓN** "
      ],
      "metadata": {
        "id": "1h9wNIu3Saet"
      },
      "id": "1h9wNIu3Saet"
    },
    {
      "cell_type": "code",
      "source": [
        "# Registro de operacion genética de cruce\n",
        "toolbox.register(\"mate\", tools.cxOnePoint)"
      ],
      "metadata": {
        "id": "MgE-AgePuCdq"
      },
      "id": "MgE-AgePuCdq",
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizamos el operador `mutGaussian` (mutación Gaussiana) con una media de 0 y una deviación típica de 5"
      ],
      "metadata": {
        "id": "F8LLceksf6MO"
      },
      "id": "F8LLceksf6MO"
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox.register(\"mutate\", tools.mutGaussian, mu=0,\n",
        "                 sigma= 5, indpb=0.1)"
      ],
      "metadata": {
        "id": "u0kE-5mMuFmD"
      },
      "id": "u0kE-5mMuFmD",
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "`MutGaussian(individual, mu, sigma, indpb)` \n",
        "\n",
        "Consiste en mutar, con una probabilidad `indpb` cada uno de los genes del individuo `individual`, sumando a cada uno de ellos una cierta cantidad aleatoria. \n",
        "\n",
        "Esta cantidad se genera según una función de probabilidad Gaussiana (o normal), de media `mu` y desviación típica `sigma`"
      ],
      "metadata": {
        "id": "4qoeqcBygDnS"
      },
      "id": "4qoeqcBygDnS"
    },
    {
      "cell_type": "markdown",
      "source": [
        "`indpb`: La probabilidad  de mutar cada uno de los genes del individuo\n",
        "\n",
        "En general, debe ser bajo para que la mutación no modifique en exceso al individuo. Valores muy altos de esta probabilidad pueden hacer que el algoritmo no converja correctamente, o que no se intensifiquen ciertas zonas del espacio de búsqueda."
      ],
      "metadata": {
        "id": "qh0Itj2ngTm_"
      },
      "id": "qh0Itj2ngTm_"
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**SELECCIÓN** "
      ],
      "metadata": {
        "id": "IU6-fGfBSUDz"
      },
      "id": "IU6-fGfBSUDz"
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox.register(\"select\", tools.selTournament, tournsize=3)"
      ],
      "metadata": {
        "id": "C7AT0zG-uIVs"
      },
      "id": "C7AT0zG-uIVs",
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usamos el operador `selTournament(individuals, k, tournsize)` que nos permite realizar una **selección mediante torneo**. \n",
        "\n",
        "Fijamos un tamaño igual a 3, puesto que se ha demostrado que este tamaño funciona relativamente bien para la mayoría de los casos.\n",
        "\n",
        "Al final del torneo, se habrán clasificiado los 3 mejores individuos."
      ],
      "metadata": {
        "id": "Nbd5GtFGgxI-"
      },
      "id": "Nbd5GtFGgxI-"
    },
    {
      "cell_type": "markdown",
      "source": [
        "La *selección mediante torneo* tiene una ventaja, y es que al ser un proceso muy elitista (puesto que solo participan en las operaciones genéticas los ganadores /mejores individuos) hace que el algoritmo converja más rápidamente."
      ],
      "metadata": {
        "id": "980lPUgBiX5K"
      },
      "id": "980lPUgBiX5K"
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_evolucion(log):\n",
        "    \"\"\"\n",
        "    Representa la evolución del mejor individuo en cada generación\n",
        "    \"\"\"\n",
        "    gen = log.select(\"gen\")\n",
        "    fit_mins = log.select(\"min\")\n",
        "    fit_maxs = log.select(\"max\")\n",
        "    fit_ave = log.select(\"avg\")\n",
        "\n",
        "    fig, ax1 = plt.subplots()\n",
        "    ax1.plot(gen, fit_mins, \"b\")\n",
        "    ax1.plot(gen, fit_maxs, \"r\")\n",
        "    ax1.plot(gen, fit_ave, \"--k\")\n",
        "    ax1.fill_between(gen, fit_mins, fit_maxs,\n",
        "                     where=fit_maxs >= fit_mins, facecolor='g', alpha = 0.2)\n",
        "    ax1.set_xlabel(\"Generation\")\n",
        "    ax1.set_ylabel(\"Fitness\")\n",
        "    ax1.set_ylim([-10, 160])\n",
        "    ax1.legend([\"Min\", \"Max\", \"Avg\"], loc=\"lower center\")\n",
        "    plt.grid(True)\n",
        "    plt.savefig(\"Convergencia.eps\", dpi = 300)"
      ],
      "metadata": {
        "id": "U5qYAHjNuKbO"
      },
      "id": "U5qYAHjNuKbO",
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Configuramos el algoritmo genético"
      ],
      "metadata": {
        "id": "QURstFAoi6Ps"
      },
      "id": "QURstFAoi6Ps"
    },
    {
      "cell_type": "code",
      "source": [
        "def main():\n",
        "    random.seed(42)\n",
        "    CXPB, MUTPB, NGEN = 0.5, 0.2, 20\n",
        "    pop = toolbox.population()\n",
        "    hof = tools.HallOfFame(1)\n",
        "    stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
        "    stats.register(\"avg\", np.mean)\n",
        "    stats.register(\"std\", np.std)\n",
        "    stats.register(\"min\", np.min)\n",
        "    stats.register(\"max\", np.max)\n",
        "    logbook = tools.Logbook()\n",
        "    pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=CXPB,\n",
        "                                       mutpb=MUTPB, ngen=NGEN, stats=stats,\n",
        "                                       halloffame=hof, verbose=True)\n",
        "    return hof, logbook"
      ],
      "metadata": {
        "id": "YaUHmlN4uOKh"
      },
      "id": "YaUHmlN4uOKh",
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Resultados del Algoritmo Genético"
      ],
      "metadata": {
        "id": "PInRNdxjTWJ9"
      },
      "id": "PInRNdxjTWJ9"
    },
    {
      "cell_type": "code",
      "source": [
        "best, log = main()\n",
        "print(\"Mejor fitness: %f\" %best[0].fitness.values)\n",
        "print(\"Mejor individuo %s\" %best[0])\n",
        "plot_evolucion(log)"
      ],
      "metadata": {
        "id": "T6ZazoG5uTJg",
        "outputId": "24410400-96d1-4fe9-afc6-88bdcdfaffde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "id": "T6ZazoG5uTJg",
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gen\tnevals\tavg        \tstd        \tmin         \tmax        \n",
            "0  \t100   \t1.93843e-50\t1.92871e-49\t-8.19747e-63\t1.93843e-48\n",
            "1  \t49    \t-0.01      \t0.0994987  \t-1          \t1.93843e-48\n",
            "2  \t52    \t9.54873e-32\t9.50086e-31\t0           \t9.54873e-30\n",
            "3  \t64    \t2.86462e-31\t1.62889e-30\t-3.56017e-85\t9.54873e-30\n",
            "4  \t58    \t-0.000366985\t0.00365146 \t-0.0366985  \t6.5714e-21 \n",
            "5  \t54    \t1.99884e-21 \t1.53568e-20\t0           \t1.53884e-19\n",
            "6  \t50    \t1.83181e-20 \t7.80529e-20\t-1.32976e-61\t3.93195e-19\n",
            "7  \t74    \t1.70604e-11 \t1.69749e-10\t-2.91446e-52\t1.70604e-09\n",
            "8  \t51    \t1.36313e-09 \t1.33926e-08\t5.33806e-108\t1.34607e-07\n",
            "9  \t50    \t5.38428e-09 \t2.63774e-08\t5.92491e-35 \t1.34607e-07\n",
            "10 \t50    \t1.8845e-08  \t4.67068e-08\t6.5714e-21  \t1.34607e-07\n",
            "11 \t65    \t4.98379e-08 \t6.49853e-08\t1.53884e-19 \t1.3565e-07 \n",
            "12 \t52    \t1.01114e-07 \t5.80737e-08\t9.20815e-18 \t1.35972e-07\n",
            "13 \t50    \t0.0662859   \t0.653976   \t2.63794e-18 \t6.57303    \n",
            "14 \t70    \t0.176375    \t1.01234    \t8.50633e-20 \t6.57303    \n",
            "15 \t60    \t0.268031    \t1.29318    \t7.77223e-67 \t6.69177    \n",
            "16 \t61    \t1.08017     \t2.41933    \t6.51648e-33 \t6.69177    \n",
            "17 \t56    \t2.82554     \t3.27183    \t-1.47281    \t7.33506    \n",
            "18 \t60    \t5.74914     \t2.3037     \t-1.3246e-05 \t7.41306    \n",
            "19 \t63    \t6.45191     \t1.32893    \t1.82147e-22 \t7.40523    \n",
            "20 \t63    \t6.62916     \t1.19754    \t-0.219969   \t7.40523    \n",
            "Mejor fitness: 7.413065\n",
            "Mejor individuo [0.06046700210138432, 1.7919972024804398]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-87-a0277e2a9551>:14: MatplotlibDeprecationWarning: The parameter where must have the same size as x in fill_between(). This will become an error in future versions of Matplotlib.\n",
            "  ax1.fill_between(gen, fit_mins, fit_maxs,\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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klWY+mEnAWWZ2BlAMDAR+AJSbWYG7twCVwPosxCYi0qdl/JyCu1/n7pXuPgaYCfzR3c8HaoAZYbPZwCOZjk1EpK/LpfsUrgG+bGa1wFBgXpbjERHpc7Jx+Cji7kuAJeHwa8CJ2YxHRKSvy6U9BRERyTIlBRERiSgpiIhIRElBREQiSgoiIhLJ6tVHIpnS3NzMunXraGpqytgyBw0axOrVqztv2AOKi4uprKykX79+GVme9F5KCtInrFu3jgEDBjBmzBiC/hfjt23bNgYMGBD7ctydLVu2sG7dOsaOHRv78qR30+Ej6ROampoYOnRoxhJCJpkZQ4cOzehekPReSgrSZ/TGhNCqN/9tkllKCiIiElFSEMkQM+OCCy6IxltaWhg+fDhnnnkmAIsWLeKWW27JVngigE40i2RMaWkpK1euZOfOnZSUlPDYY48xatSoqP6ss87irLPOymKEIkoK0gddeSU8/3zPznP8ePj+9ztvd8YZZ/A///M/zJgxg4ULFzJr1iyefPJJAO69916WLVvGXXfdxZw5cxg4cCDLli1j48aN3HbbbcyYMaOTuYt8cDp8JJJBM2fOpLq6mqamJl544QVOOumkDttu2LCBp556isWLF3PttddmMErpy7SnIH1OV/6jj8sxxxzD2rVrWbhwIWecccY+25599tnk5eVxxBFHsGnTpgxFKH2dkoJIhp111llcddVVLFmyhC1btnTYrqioKBoOHqsrEr+MJwUzGw38HKgAHLjH3X9gZkOA+4AxwFrgPHffmun4ROJ20UUXUV5eztFHH82SJUuyHY7IHrJxTqEF+Iq7HwGcDFxmZkcA1wKPu/vBwOPhuEivU1lZyeWXX57tMETalfE9BXffAGwIh7eZ2WpgFDAdmBw2W0DwmM5rMh2fSFwaGxv3Kps8eTKTJ08GYM6cOcyZMwcIrkTqbFqROFg2j1Wa2RjgCeAo4E13Lw/LDdjaOt5mmrnAXICKioqq6urqtJbd2NhIWVlZeoHHKFfjgtyNrStxDRo0iIMOOihDEQUSiQT5+fkZW15tbS319fWdtvswf47Z0BvjmjJlynJ3n9Bupbtn5QWUAcuBz4Tj77Wp39rZPKqqqjxdNTU1aU8bp1yNyz13Y+tKXC+99FL8gbTR0NCQ0eV19W/8MH+O2dAb4wKWeQfb1azcp2Bm/YBfAb9091+HxZvMbGRYPxKoy0ZsIiJ9WcaTQnhoaB6w2t2/l1K1CJgdDs8GHsl0bCIifV027lOYBPwL8KKZtXY2cD1wC3C/mV0MvAGcl4XYRET6tGxcffQU0FHn71MzGYuIiOxJfR+JZEhnXWeL5AIlBZEMSe06G9ir62yRXKC+j6TvyWLf2fvqOvuZZ57hiiuuoKmpiZKSEn72s59x6KGHcscdd/Diiy8yf/58XnzxRWbNmsUzzzxD//79e/ZvEEF7CiIZta+usw877DCefPJJnnvuOW666Sauv/56AK644gpqa2t56KGHuPDCC7n77ruVECQ22lOQvieLfWfvq+vs+vp6Zs+ezZo1azAzmpubAcjLy+Pee+/lmGOO4fOf/zyTJk3KRujSR2hPQSTDWrvOnjVr1h7l3/jGN5gyZQorV67kN7/5DU1NTVHdmjVrKCsr4+233850uNLHKCmIZNhFF13EDTfcwNFHH71HeX19fXTiObVDvPr6ei6//HKeeOIJtmzZwoMPPpjJcKWPUVIQybCOus6++uqrue666zjuuONoaWmJyr/0pS9x2WWXccghhzBv3jyuvfZa6urUC4zEQ+cURDKks66zJ06cyCuvvBLVffvb3wZg/vz5Udno0aOpra2NN1Dp05QURKTvcYemJmhogJ07IZkMXu7vD4fjpa++CuXl7dbR0aMHrJ1OG7pa1kVFMe0tKimISCCRgNdfDzZ46WjdaCYSwat1uKP3NmVDX3wRtm1Lb7mtG/iGhmAercPhy7dtg4Z6aHi/zhKJLs3+hO5H1GWtEeQDSeA9gkdTtoR1LcBgoBxoAl4Oy0qAUbNmwXk930WckoJIX+cOjz6Kf/Wr2EsvZS2Moztv0mXJ/iUkBpSRGFBKoix8jRpG8tAD3i8bUEqytJRkSRGelxf8156Xh+cZWDDuecZ76xso33/wXuVuRl39NvLzjGGDBpJIJnl06XM0bt/Jtp07aQjfTz7iYKadMJ6G7Ts4/+Yf0riziW07dtK4s4mkO1eeewaXTj+dtzdvZfKXv7nX3/K1889h9idOZc26DXzq+lsBOP6gsfx42jT278F11kpJQaQvW7EiSAZ//CO7x45m4/+7lsTAD/CUsby8YAObn4fn54GF73kdjOflBxvbvDy2vPYuQw8cmtZik0WF4Ya+jGRpCfTgE+82rdoER1YAUPtyLcv/upwVf13Bc0uf493N7zJ91nS+cfs3cHeumv0lkuGeVn5+PmUDyig95nA+etokdu/azUce+h1lA8ooG1hGaVkp/Qr7ccTEKhpOPp687Tu4qnE7efl55Ofnk1+QT0F+AUeMP4KGQ8ZR1rid2yuGk5+fT8XwCnb0H9ljf2MqJQWRvujNN+HrX4df/ILEkHLe/tZVbL7gXLywX9ZCaijYREm48c0FiUSCNavXsGbpGj595KcB+OrFX+WttW9RsV8FJ33sJI6uOppDjzwUCDo8vL/mfkrLSikbWEZxSTGWcs6gsKiQ787/bofL61/an5kXz+ywvrSslMnTJgfDhaVsXLmxB/7KvSkpiPQl9fWM/elP8V/9Csepu2w2G75wIckPsnfQi9S+XMtfl/w12BN45jkaGxoZOGggZ150JmbGDd+/geEVw9lv9H57bPBbjTloTOaD7mFKCiIZ9PDDD3POOeewevVqDjvssMwtuLkZ7r4b/+Y3OWDzZrZ85pO8fc2l7K6M5xBErkskEmx6exNvvvYmL698mfP/7/n0K+zHoupF/PdP/5sDDjyAj3/64xx/8vHsP2T/KAGMP2F8liOPn5KCSAYtXLiQU045hYULF/LNb+59UrHHucPDD+PXXIOtWUPjpAn87ZvfoOTs3t9/kruzedNm3nz9TQ467CAGDR7EU48/xZ3fvpO31r5F8+7mqO3JHzuZw44+jAs+fwGfu/RzDBsxLKrbtGpTNsLPmpxLCmY2DfgBwVVa/+nut2Q5JOmFWm8YS3Xeeedx6aWXsmPHjr06qwOYM2cOc+bMYfPmzcyYMWOPuiVLlnS6zMbGRp566ilqamr49Kc/zcSJE5k3bx4PPPBANI/bb7+dxYsXM2/ePG699VbKy8s59thjKSoq4q677ureH/n00/hVV2F//jNNh4xj3YLv0zB1Eg0v1VHSvTnlrF1Nu9hWv43ikmLKBpaxtnYtd99+N2+89gZvvf4WO3cEz6747s++y6mnn8rAQQPZf9z+TJo6iQPGHcD+4/Zn3CHjKB9SDsCIkSOy+efkhJxKCmaWD/wI+DiwDnjWzBa5e/aukxPpIY888gjTpk3jkEMOYejQoQwePJilS5eyfft2SktLue+++5g5cyZvv/023/rWt1ixYgUDBgzgtNNO49hjj+36gl57Da67Du6/n5bhQ3n7tq+x+Z8/DQWZ/7m7+x5X4wDs3LGTZDKJJ4O6ZDJJYVEhAM27m3n2z8/SUN9Aw3vBa1v9Nk78hxOZdNok6jbU8cULvhjV7WraBcDVN1/NeXOCa/ZXv7ia/cftHxz6Gbc/+4/dn8OPORyAYyYcw+3zbs/0avhQMe/ojrwsMLOJwI3u/olw/DoAd/9Oe+0nTJjgy5Yt6/ZyXrhzCQu+ejFP+btYHoRXxFGUl8evDjmWfv3gB5ve4I/1W/eYblBBAb866igAbnz9df5cXw9A6xr8SGEh9x56BM3NcM1rtTy/fVtw42MSkg6j8kr4eslheBK+s+MVXk9u32P+B+aV8pXCcRQUFHDDjtVsSDbtUX9k/kCuKDkQgGu3r+Jd3/3+ugOqCsr5fP+xAFyx7QV2+p4350wqHMqckuDK5ksanifR5rM/rd9wZhZXstuTXNb4t73W2yfyh3NuSSXbks18ZcfKverPKdyPTxZW8E5yF1/bsXcen1VYyZTC4byV2Mm3dr68V/2FRQcwsd8QahON3LZzzV71lxSP5fiCcla2NHBn06tRubtjZlxZfCBHFAxkWfNW7tm1do9pb/3NfRw3YjRFlkejt7A5Zd212s+KKbQ8Grxlj3XbqjKvhAKM97yZ97x5r/rReSXkY2z1Zuq9OfhipJyL/MqVX+LSz36O8SedwI8X/oINGzfSuL2RkyacyCemTuWMs6ez7IHfsGjpUyyu+V++882bAPiv6oW88cab/PS64HBTXXIXO9jzs+1HHju2bMX/+UYWvPcky4CNhftRV7QfCYzi4nGMPfgnALy6+vPs2v0y4DhJcOhfelRUv2b1eTTtfBVI4B58gQcMmsTYg+8GYNXzp9C8e304bRL3JOVDP8W4g+8B4PlnxtHSspXgdqzAsBEXMOagHwGw7C9DoE38I0ZewkdGfIv84maeW7rfHnV5eaWMrLyKkZVfpqWlnrW1l1JQUE5+weDwvZyKUSdxyOEHMbJyFyNH72a/yt2MrNxF2cA0b8RLsWnVJsoP/Agb1xeyYV0hG9YV8fZbwfCWun4d3tTcFfkFkF/gFLS++jn5+cF763hBm/H8fKd/cT9GD13PlVcel9ZyzWy5u09ory6n9hSAUcBbKePrgJNSG5jZXGAuQEVFRZd229uqW70G9wS7W5LBTZhheQHOCyuCjsheJUEdCQwHC+5r2ZXnrHqhkfx8Z/22Jjbvbg7udMfAIen5/GVTMP0GEryX8qMwYKclaGYXeXlOItlCIrnnDyNBC5ZsJtnc0kF9M8nmJnBoSbaQaLPRb25poWVn8J9TSyJBc8rycdi9u5ndyV1g0JxIkGCPbRYtLc207GqixZN7LRvA81pINjeR9L1jA0gmdgf1yd3t1iei+qb261t2kaSJZHJX+/Nv2U3Sm0gm2ql3SET17S0/2AAGPRN00D2BOe7JDuvdk+Fn3cH07ji+5/Th23v19fzp2aWsqn0FN6MlkQCDm//tRhY+cD/lAwdy3OFHUta/P44HE0bzCMbdWz/PvZfvYVmieTdrig9nZV5/mq0I8zwMwxOF7G4Mps/LKyc/bzhBf5iGmZHHiKi+X/5BUFSKWR6Qj1kehQVHRvUDyj5OItEA5EVtSoqPiuqHDr0E910p9XmUlLxfP3LkjWHU79cXFx9JosXwHcUceODvyM8vJz9/MPn55eTlBXsRwfQD2L/yF21XO3Vv9uOFZ4rZtWvP+xPKypoZUbGTESN2UPGRneHwznB4B2VlLZhBU1MedXUl1G3sT11dCZs2lVC3sSQY3ngkW7cW7zHf/PwkI0bsYsiQ3eTnp5cVghvAjZYWo6Uln5YWI5E0Ei2tZUYiYSQSeSnj7/dhOmNGA+PHL0lr2fuSa3sKM4Bp7v5/wvF/AU5y9y+01z7dPQUIjt+2HlfetQs2b4Z33oG6uo7ePRrfts0oKnKGj4CKETB8uDFiBAwfTofvpaXdjyvX5GpsXYlr9erVHH744ZkJKLRt2zYGDBgAwD333MPy5cu5++67o/pTTz2Vm266idmzZ3PCCSfwT//0T5x33nmsX7+eSZMm8dxzzzFgwACmTp3K0Ucf3ek5ha7+jR/mz3Ff3IPf8dq1weuNN94fXrvWWbsWtm/f8zLS0rIERUXOu1v2/P+4Xz/ngAPggAOMoqINTJw4kgMOgDFjgtd++/Xo/XFd5h70CtLcDE8++SdOP/3UtObzYdpTWA+MThmvDMtiVVQEo0YFr469/2VqboaCAvsgfVlJH7Nw4UKuueaaPcrOPfdcqqurOfPMM7n33ntZsGABAKNGjeL666/nxBNPZMiQIRx22GEMGjQoG2F/qJgF/4ANHw4n7NVhkeEO777bNmHk09T0/sa+dcM/cqSRF/5TvmTJ35k8OTcu3TULTg0VFEBhYTz/0OdaUngWONjMxhIkg5nAZ7Mb0t76Ze+mT/mQqqmp2ass9ZkKbfcCPvvZzzJ37lxaWlo455xzOPvss2OPsbczg6FDg1dVVbajyV059ZAdd28BvgD8HlgN3O/uq7IblUjm3XjjjYwfP56jjjqKsWPHKshGipAAAAhbSURBVClIxuTangLu/lvgt9mOQySbbr9dl01KduTUnoJInHLpooqe1pv/NsksJQXpE4qLi9myZUuv3Hi6O1u2bKG4uLjzxiKdyLnDRyJxqKysZN26dbzzzjsZW2ZTU1PGNtTFxcVUVlZmZFnSuykpSJ/Qr18/xo4dm9FlLlmyhOOOS++OU5Fs0eEjERGJKCmIiEhESUFERCI51fdRd5nZO8AbaU4+DNjcg+H0lFyNC3I3NsXVPYqre3pjXAe4+/D2Kj7USeGDMLNlHXUIlU25GhfkbmyKq3sUV/f0tbh0+EhERCJKCiIiEunLSeGebAfQgVyNC3I3NsXVPYqre/pUXH32nIKIiOytL+8piIhIG0oKIiIS6fVJwcymmdnfzazWzK5tp77IzO4L65ea2ZgMxDTazGrM7CUzW2VmV7TTZrKZ1ZvZ8+Hr3+KOK1zuWjN7MVzmXg/AtsCd4fp6wcyOz0BMh6ash+fNrMHMrmzTJmPry8zmm1mdma1MKRtiZo+Z2ZrwfXAH084O26wxs9kZiOvfzezl8LN6yMzKO5h2n597DHHdaGbrUz6vMzqYdp+/3xjiui8lprVm9nwH08ayvjraNmT0++XuvfYF5AOvAuOAQuBvwBFt2lwK/CQcngncl4G4RgLHh8MDgFfaiWsysDgL62wtMGwf9WcAjxI8tPpkYGkWPtONBDffZGV9AR8DjgdWppTdBlwbDl8L3NrOdEOA18L3weHw4JjjOh0oCIdvbS+urnzuMcR1I3BVFz7rff5+ezquNvXfBf4tk+uro21DJr9fvX1P4USg1t1fc/fdQDUwvU2b6cCCcPhBYKqZWZxBufsGd18RDm8jePToqDiX2YOmAz/3wNNAuZll8qnmU4FX3T3dO9k/MHd/Ani3TXHq92gB0N7zMz8BPObu77r7VuAxYFqccbn7Hzx4zC3A00DG+9fuYH11RVd+v7HEFW4DzgMW9tTyuhhTR9uGjH2/entSGAW8lTK+jr03vlGb8MdTDwzNSHRAeLjqOGBpO9UTzexvZvaomR2ZoZAc+IOZLTezue3Ud2WdxmkmHf9Qs7G+WlW4+4ZweCNQ0U6bbK+7iwj28trT2ecehy+Eh7Xmd3A4JJvr6x+ATe6+poP62NdXm21Dxr5fvT0p5DQzKwN+BVzp7g1tqlcQHCI5Fvgh8HCGwjrF3Y8HPglcZmYfy9ByO2VmhcBZwAPtVGdrfe3Fg335nLrW28y+BrQAv+ygSaY/9x8DBwLjgQ0Eh2pyySz2vZcQ6/ra17Yh7u9Xb08K64HRKeOVYVm7bcysABgEbIk7MDPrR/Ch/9Ldf9223t0b3L0xHP4t0M/MhsUdl7uvD9/rgIcIduFTdWWdxuWTwAp339S2IlvrK8Wm1sNo4XtdO22ysu7MbA5wJnB+uEHZSxc+9x7l7pvcPeHuSeCnHSwvW+urAPgMcF9HbeJcXx1sGzL2/ertSeFZ4GAzGxv+lzkTWNSmzSKg9Sz9DOCPHf1wekp4vHIesNrdv9dBm4+0ntswsxMJPqtYk5WZlZrZgNZhgpOUK9s0WwR8zgInA/Upu7Vx6/C/t2ysrzZSv0ezgUfaafN74HQzGxweLjk9LIuNmU0DrgbOcvcdHbTpyufe03Glnoc6p4PldeX3G4d/BF5293XtVca5vvaxbcjc96unz57n2ovgaplXCK5i+FpYdhPBjwSgmOBwRC3wDDAuAzGdQrD79wLwfPg6A7gEuCRs8wVgFcEVF08DH81AXOPC5f0tXHbr+kqNy4AfhevzRWBChj7HUoKN/KCUsqysL4LEtAFoJjhuezHBeajHgTXA/wJDwrYTgP9Mmfai8LtWC1yYgbhqCY4zt37PWq+02w/47b4+95jj+kX4/XmBYIM3sm1c4fhev9844wrL7239XqW0zcj62se2IWPfL3VzISIikd5++EhERLpBSUFERCJKCiIiElFSEBGRiJKCiIhElBSkTzGzCjP7bzN7Leyi4K9mdk6WYplsZh9NGb/EzD6XjVhEWhVkOwCRTAlvDHoYWODunw3LDiDoOiOuZRb4+x3StTUZaAT+AuDuP4krDpGu0n0K0meY2VSCrpBPbacuH7iFYENdBPzI3e82s8kE3TxvBo4ClgMXuLubWRXwPaAsrJ/j7hvMbAnBTUenENwg9QrwdYLun7cA5wMlBDfZJYB3gC8S9ADb6O63m9l44CdAf4Ibty5y963hvJcCU4Byghuunuy5tSR9nQ4fSV9yJEHHee25mKDLjhOAE4D/a2Zjw7rjgCsJ+rUfB0wK+6f5ITDD3auA+cDNKfMrdPcJ7v5d4CngZHc/jqD756vdfS3BRv8Odx/fzob958A17n4MwZ2/N6TUFbj7iWFMNyDSg3T4SPosM/sRwX/zu4E3gGPMbEZYPQg4OKx7xsN+cMIncY0B3iPYc3gs7HIpn6DLhFapnalVAveF/f0UAq93EtcgoNzd/xQWLWDPnmFbO0lbHsYi0mOUFKQvWQWc2zri7peFPakuA94Evujue3QgFh4+2pVSlCD43Riwyt0ndrCs7SnDPwS+5+6LUg5HfRCt8bTGItJjdPhI+pI/AsVm9q8pZf3D998D/xoeFsLMDgl7wOzI34HhZjYxbN9vHw/2GcT7XRinPjd3G8EjF/fg7vXAVjP7h7DoX4A/tW0nEgf9lyF9Rnhy+GzgDjO7muAE73bgGoLDM2OAFeFVSu/Q/iMPW+e1OzzUdGd4uKcA+D7B3khbNwIPmNlWgsTUeq7iN8CDZjad4ERzqtnAT8ysP8Gzdi/s/l8s0n26+khERCI6fCQiIhElBRERiSgpiIhIRElBREQiSgoiIhJRUhARkYiSgoiIRP4/E0mbBMQxPd4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cruce con dos puntos"
      ],
      "metadata": {
        "id": "Qxd99VO4m7wq"
      },
      "id": "Qxd99VO4m7wq"
    },
    {
      "cell_type": "code",
      "source": [
        "# Registro de operacion genética de cruce\n",
        "#toolbox.register(\"mate\", tools.cxOnePoint)\n",
        "toolbox.register(\"mate\", tools.cxTwoPoint)\n",
        "#toolbox.register(\"mate\", tools.cxUniform)"
      ],
      "metadata": {
        "id": "UhzMa--3nGiL"
      },
      "id": "UhzMa--3nGiL",
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox.register(\"mutate\", tools.mutGaussian, mu=0,\n",
        "                 sigma= 5, indpb=0.1)"
      ],
      "metadata": {
        "id": "qHXehm4GnRay"
      },
      "id": "qHXehm4GnRay",
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox.register(\"select\", tools.selTournament, tournsize=3)"
      ],
      "metadata": {
        "id": "QaeYfRROnVQO"
      },
      "id": "QaeYfRROnVQO",
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_evolucion(log):\n",
        "    \"\"\"\n",
        "    Representa la evolución del mejor individuo en cada generación\n",
        "    \"\"\"\n",
        "    gen = log.select(\"gen\")\n",
        "    fit_mins = log.select(\"min\")\n",
        "    fit_maxs = log.select(\"max\")\n",
        "    fit_ave = log.select(\"avg\")\n",
        "\n",
        "    fig, ax1 = plt.subplots()\n",
        "    ax1.plot(gen, fit_mins, \"b\")\n",
        "    ax1.plot(gen, fit_maxs, \"r\")\n",
        "    ax1.plot(gen, fit_ave, \"--k\")\n",
        "    ax1.fill_between(gen, fit_mins, fit_maxs,\n",
        "                     where=fit_maxs >= fit_mins, facecolor='g', alpha = 0.2)\n",
        "    ax1.set_xlabel(\"Generation\")\n",
        "    ax1.set_ylabel(\"Fitness\")\n",
        "    ax1.set_ylim([-10, 160])\n",
        "    ax1.legend([\"Min\", \"Max\", \"Avg\"], loc=\"lower center\")\n",
        "    plt.grid(True)\n",
        "    plt.savefig(\"Convergencia.eps\", dpi = 300)"
      ],
      "metadata": {
        "id": "9RXaLl0Dn9xc"
      },
      "id": "9RXaLl0Dn9xc",
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def main():\n",
        "    random.seed(42)\n",
        "    CXPB, MUTPB, NGEN = 0.5, 0.2, 20\n",
        "    pop = toolbox.population()\n",
        "    hof = tools.HallOfFame(1)\n",
        "    stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
        "    stats.register(\"avg\", np.mean)\n",
        "    stats.register(\"std\", np.std)\n",
        "    stats.register(\"min\", np.min)\n",
        "    stats.register(\"max\", np.max)\n",
        "    logbook = tools.Logbook()\n",
        "    pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=CXPB,\n",
        "                                       mutpb=MUTPB, ngen=NGEN, stats=stats,\n",
        "                                       halloffame=hof, verbose=True)\n",
        "    return hof, logbook"
      ],
      "metadata": {
        "id": "jemaIA6aoD2_"
      },
      "id": "jemaIA6aoD2_",
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best, log = main()\n",
        "print(\"Mejor fitness: %f\" %best[0].fitness.values)\n",
        "print(\"Mejor individuo %s\" %best[0])\n",
        "plot_evolucion(log)"
      ],
      "metadata": {
        "id": "AjQhdO8OoHNg",
        "outputId": "26124845-34eb-4c58-f12d-4abcbfdccca4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "id": "AjQhdO8OoHNg",
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-93-a0277e2a9551>:14: MatplotlibDeprecationWarning: The parameter where must have the same size as x in fill_between(). This will become an error in future versions of Matplotlib.\n",
            "  ax1.fill_between(gen, fit_mins, fit_maxs,\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gen\tnevals\tavg        \tstd        \tmin         \tmax        \n",
            "0  \t100   \t1.93843e-50\t1.92871e-49\t-8.19747e-63\t1.93843e-48\n",
            "1  \t55    \t9.69215e-50\t4.2247e-49 \t-5.6445e-140\t1.93843e-48\n",
            "2  \t54    \t-2.77757e-10\t2.76365e-09\t-2.77757e-08\t9.62191e-38\n",
            "3  \t54    \t1.39112e-25 \t1.38414e-24\t0           \t1.39112e-23\n",
            "4  \t43    \t-2.77757e-10\t2.76365e-09\t-2.77757e-08\t1.39112e-23\n",
            "5  \t61    \t9.2011e-15  \t6.63727e-14\t0           \t5.84792e-13\n",
            "6  \t64    \t-8.5687e-05 \t0.000852575\t-0.0085687  \t5.84792e-13\n",
            "7  \t61    \t0.00101441  \t0.0100932  \t4.41527e-65 \t0.101441   \n",
            "8  \t51    \t0.0653251   \t0.617144   \t-6.94448e-08\t6.20338    \n",
            "9  \t55    \t0.197282    \t1.05693    \t-6.23852e-14\t6.20338    \n",
            "10 \t58    \t0.617602    \t1.87859    \t-2.43588    \t6.20338    \n",
            "11 \t59    \t1.79351     \t2.75949    \t-9.91273e-17\t6.20338    \n",
            "12 \t52    \t4.34659     \t2.78739    \t-0.491237   \t6.20338    \n",
            "13 \t66    \t5.90065     \t1.27026    \t9.62e-33    \t6.20338    \n",
            "14 \t60    \t5.84401     \t1.62766    \t-4.9379     \t6.20338    \n",
            "15 \t65    \t5.86283     \t1.56723    \t-4.88031    \t6.20338    \n",
            "16 \t61    \t5.77535     \t1.79677    \t-6.35773    \t6.20338    \n",
            "17 \t70    \t6.20338     \t1.77636e-15\t6.20338     \t6.20338    \n",
            "18 \t59    \t5.96391     \t1.17557    \t-0.000153669\t6.20338    \n",
            "19 \t56    \t5.98988     \t1.0765     \t6.85437e-25 \t6.20338    \n",
            "20 \t61    \t5.8461      \t1.38283    \t4.75508e-82 \t6.20338    \n",
            "Mejor fitness: 6.203384\n",
            "Mejor individuo [0.11722261005967027, 1.9495037203322996]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cruce uniforme"
      ],
      "metadata": {
        "id": "ngZpeoptnAsX"
      },
      "id": "ngZpeoptnAsX"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora probaremos lo mismo que antes, pero con un cruce uniforme. Los atributos se intercambian según la probabilidad indpb, que en este caso, le hemos dado el valor de 0.4"
      ],
      "metadata": {
        "id": "6rRh0dK7tTfG"
      },
      "id": "6rRh0dK7tTfG"
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox.register(\"mate\", tools.cxUniform, indpb=0.4)"
      ],
      "metadata": {
        "id": "ZmGBhvNlnKib"
      },
      "id": "ZmGBhvNlnKib",
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox.register(\"mutate\", tools.mutGaussian, mu=0,\n",
        "                 sigma= 5, indpb=0.1)"
      ],
      "metadata": {
        "id": "GL2wWicEnR-m"
      },
      "id": "GL2wWicEnR-m",
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "toolbox.register(\"select\", tools.selTournament, tournsize=3)"
      ],
      "metadata": {
        "id": "SGO4E_fInVwC"
      },
      "id": "SGO4E_fInVwC",
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_evolucion(log):\n",
        "    \"\"\"\n",
        "    Representa la evolución del mejor individuo en cada generación\n",
        "    \"\"\"\n",
        "    gen = log.select(\"gen\")\n",
        "    fit_mins = log.select(\"min\")\n",
        "    fit_maxs = log.select(\"max\")\n",
        "    fit_ave = log.select(\"avg\")\n",
        "\n",
        "    fig, ax1 = plt.subplots()\n",
        "    ax1.plot(gen, fit_mins, \"b\")\n",
        "    ax1.plot(gen, fit_maxs, \"r\")\n",
        "    ax1.plot(gen, fit_ave, \"--k\")\n",
        "    ax1.fill_between(gen, fit_mins, fit_maxs,\n",
        "                     where=fit_maxs >= fit_mins, facecolor='g', alpha = 0.2)\n",
        "    ax1.set_xlabel(\"Generation\")\n",
        "    ax1.set_ylabel(\"Fitness\")\n",
        "    ax1.set_ylim([-10, 160])\n",
        "    ax1.legend([\"Min\", \"Max\", \"Avg\"], loc=\"lower center\")\n",
        "    plt.grid(True)\n",
        "    plt.savefig(\"Convergencia.eps\", dpi = 300)"
      ],
      "metadata": {
        "id": "tGXESCqzr2EC"
      },
      "id": "tGXESCqzr2EC",
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def main():\n",
        "    random.seed(42)\n",
        "    CXPB, MUTPB, NGEN = 0.5, 0.2, 20\n",
        "    pop = toolbox.population()\n",
        "    hof = tools.HallOfFame(1)\n",
        "    stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
        "    stats.register(\"avg\", np.mean)\n",
        "    stats.register(\"std\", np.std)\n",
        "    stats.register(\"min\", np.min)\n",
        "    stats.register(\"max\", np.max)\n",
        "    logbook = tools.Logbook()\n",
        "    pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=CXPB,\n",
        "                                       mutpb=MUTPB, ngen=NGEN, stats=stats,\n",
        "                                       halloffame=hof, verbose=True)\n",
        "    return hof, logbook"
      ],
      "metadata": {
        "id": "7cWjx6rioEh_"
      },
      "id": "7cWjx6rioEh_",
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best, log = main()\n",
        "print(\"Mejor fitness: %f\" %best[0].fitness.values)\n",
        "print(\"Mejor individuo %s\" %best[0])\n",
        "plot_evolucion(log)"
      ],
      "metadata": {
        "id": "TA7FTBvJoH2m",
        "outputId": "05f6eb19-128b-4dce-8b2c-a3369b6bcad4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "id": "TA7FTBvJoH2m",
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-99-a0277e2a9551>:14: MatplotlibDeprecationWarning: The parameter where must have the same size as x in fill_between(). This will become an error in future versions of Matplotlib.\n",
            "  ax1.fill_between(gen, fit_mins, fit_maxs,\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gen\tnevals\tavg        \tstd        \tmin         \tmax        \n",
            "0  \t100   \t1.93843e-50\t1.92871e-49\t-8.19747e-63\t1.93843e-48\n",
            "1  \t48    \t1.16306e-49\t4.60351e-49\t0           \t1.93843e-48\n",
            "2  \t58    \t9.62191e-40\t9.57368e-39\t-8.02396e-118\t9.62191e-38\n",
            "3  \t57    \t-2.77757e-10\t2.76365e-09\t-2.77757e-08 \t1.39112e-23\n",
            "4  \t57    \t2.79554e-25 \t1.94742e-24\t-6.09907e-315\t1.39112e-23\n",
            "5  \t53    \t1.30985e-21 \t1.30258e-20\t6.05943e-77  \t1.30915e-19\n",
            "6  \t66    \t1.11789e-14 \t7.83303e-14\t1.93843e-48  \t5.84792e-13\n",
            "7  \t58    \t4.6272e-14  \t1.56966e-13\t-1.87111e-79 \t5.84792e-13\n",
            "8  \t61    \t1.45692e-13 \t2.52369e-13\t-2.42373e-67 \t5.84792e-13\n",
            "9  \t60    \t0.000145264 \t0.00144536 \t1.06184e-29  \t0.0145264  \n",
            "10 \t59    \t0.000145424 \t0.00144534 \t8.38211e-41  \t0.0145264  \n",
            "11 \t63    \t0.000720799 \t0.00588412 \t5.84792e-13  \t0.0574737  \n",
            "12 \t60    \t0.00613212  \t0.0259094  \t5.76469e-15  \t0.224583   \n",
            "13 \t59    \t0.0182194   \t0.0441254  \t5.84792e-13  \t0.224583   \n",
            "14 \t55    \t0.0851464   \t0.581647   \t-2.68795     \t4.93978    \n",
            "15 \t67    \t0.239796    \t0.716749   \t6.21552e-21  \t4.93978    \n",
            "16 \t49    \t0.48714     \t0.981411   \t0.0574737    \t4.93978    \n",
            "17 \t67    \t1.14089     \t1.51474    \t1.4988e-28   \t4.93978    \n",
            "18 \t66    \t2.20067     \t1.96263    \t-1.76261     \t4.93978    \n",
            "19 \t68    \t3.857       \t1.70831    \t-1.06883e-05 \t4.93978    \n",
            "20 \t71    \t4.62082     \t1.1436     \t7.8261e-84   \t4.93978    \n",
            "Mejor fitness: 4.939777\n",
            "Mejor individuo [0.11722261005967027, 2.0947319163367135]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conclusiones"
      ],
      "metadata": {
        "id": "1ZpvjEmIcUj3"
      },
      "id": "1ZpvjEmIcUj3"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compararemos las gráficas obtenidas según los 3 tipos distintos de cruces que hemos utilizados y analizaremos lo que significan."
      ],
      "metadata": {
        "id": "ev_woU7ilFkq"
      },
      "id": "ev_woU7ilFkq"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Un único punto de cruce**\n",
        "\n",
        "Mejor fitness: 7.413065\n",
        "\n",
        "Mejor individuo [0.06046700210138432, 1.7919972024804398]"
      ],
      "metadata": {
        "id": "XMp_Q8YNk0Od"
      },
      "id": "XMp_Q8YNk0Od"
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "Ckuof9utjsE5"
      },
      "id": "Ckuof9utjsE5"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dos único punto de cruce**\n",
        "\n",
        "Mejor fitness: 6.203384\n",
        "\n",
        "Mejor individuo [0.11722261005967027, 1.9495037203322996]"
      ],
      "metadata": {
        "id": "JoA69HCOk_Zx"
      },
      "id": "JoA69HCOk_Zx"
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "WReYsGI0kmVa"
      },
      "id": "WReYsGI0kmVa"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cruce uniforme**\n",
        "\n",
        "Mejor fitness: 4.939777\n",
        "\n",
        "Mejor individuo [0.11722261005967027, 2.0947319163367135]"
      ],
      "metadata": {
        "id": "n6inhodPlCQu"
      },
      "id": "n6inhodPlCQu"
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "BMwwLt0Ukuyo"
      },
      "id": "BMwwLt0Ukuyo"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos concluir que los **máximos** de la función \"picos\"\n",
        "\n",
        "\n",
        "$$z = f(x,y) = 3(1-x)^2e^{-(x^2)-(y+1)^2}-10(x/5-x^3-y^5)e^{-x^2-y^2}-1/3e^{-(x+1)^2-y^2}$$\n",
        "\n",
        "\n",
        "En el dominio \n",
        "\n",
        "$$\\{x,y\\} \\in [-100,100]$$\n",
        "\n",
        "\n",
        "El método de cruce que mejores resultados produce es aquel de solo un punto de cruce puesto que su mejor fitness (que representa un punto máximo de la función) tiene un valor de 7.413065, y puesto que nuestro objetivo es encontrar los máximos de la función es el método que mejor cumple la función que buscamos. "
      ],
      "metadata": {
        "id": "jntTmqJxccz7"
      },
      "id": "jntTmqJxccz7"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": []
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}