Delete EA.ipynb

EA.ipynb

-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# EA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cal_pop_fitness(equation_inputs, pop):\n",
-    "    # Calculating the fitness value of each solution in the current population.\n",
-    "    # The fitness function calulates the sum of products between each input and its corresponding weight.\n",
-    "    fitness = numpy.sum(pop*equation_inputs, axis=1)\n",
-    "    return fitness\n",
-    "\n",
-    "def select_mating_pool(pop, fitness, num_parents):\n",
-    "    # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.\n",
-    "    parents = numpy.empty((num_parents, pop.shape[1]))\n",
-    "    for parent_num in range(num_parents):\n",
-    "        max_fitness_idx = numpy.where(fitness == numpy.max(fitness))\n",
-    "        max_fitness_idx = max_fitness_idx[0][0]\n",
-    "        parents[parent_num, :] = pop[max_fitness_idx, :]\n",
-    "        fitness[max_fitness_idx] = -99999999999\n",
-    "    return parents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def crossover(parents, offspring_size):\n",
-    "    offspring = numpy.empty(offspring_size)\n",
-    "    # The point at which crossover takes place between two parents. Usually, it is at the center.\n",
-    "    crossover_point = numpy.uint8(offspring_size[1]/2)\n",
-    "\n",
-    "    for k in range(offspring_size[0]):\n",
-    "        # Index of the first parent to mate.\n",
-    "        parent1_idx = k%parents.shape[0]\n",
-    "        # Index of the second parent to mate.\n",
-    "        parent2_idx = (k+1)%parents.shape[0]\n",
-    "        # The new offspring will have its first half of its genes taken from the first parent.\n",
-    "        offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point]\n",
-    "        # The new offspring will have its second half of its genes taken from the second parent.\n",
-    "        offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]\n",
-    "    return offspring"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mutation(offspring_crossover, num_mutations=1):\n",
-    "    mutations_counter = numpy.uint8(offspring_crossover.shape[1] / num_mutations)\n",
-    "    # Mutation changes a number of genes as defined by the num_mutations argument. The changes are random.\n",
-    "    for idx in range(offspring_crossover.shape[0]):\n",
-    "        gene_idx = mutations_counter - 1\n",
-    "        for mutation_num in range(num_mutations):\n",
-    "            # The random value to be added to the gene.\n",
-    "            random_value = numpy.random.uniform(-1.0, 1.0, 1)\n",
-    "            offspring_crossover[idx, gene_idx] = offspring_crossover[idx, gene_idx] + random_value\n",
-    "            gene_idx = gene_idx + mutations_counter\n",
-    "    return offspring_crossover"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
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-   "mimetype": "text/x-python",
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-   "nbconvert_exporter": "python",
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