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{
"cells": [
{
"cell_type": "markdown",
"id": "2fd45b3a-9a24-4782-812c-08223edb750e",
"metadata": {},
"source": [
"# Prueba del algoritmo genetico"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6b4829a-9001-410c-b20c-01c65c777d8a",
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{
"cell_type": "code",
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"id": "2c3c85e0-a90c-4fda-86f7-778d7328c74d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "511ff788-0d1a-4ac7-9575-de182d236574",
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"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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},
{
"cell_type": "code",
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"id": "b3ad92de-b2ed-4f21-a696-1fa2981f89dc",
"metadata": {},
"outputs": [],
"source": [
"def genetic_algorithm(population, fitness_fn, ngen=100, pmut=0.1):\n",
" \"Algoritmo Genetico \"\n",
" \n",
" popsize = len(population)\n",
" evaluate_population(population, fitness_fn) # evalua la poblacion inicial\n",
" ibest = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:1]\n",
" bestfitness = [population[ibest[0]].fitness]\n",
" print(\"Poblacion inicial, best_fitness = {}\".format(population[ibest[0]].fitness))\n",
" \n",
" for g in range(ngen): # Por cada generacion\n",
" \n",
" ## Selecciona las parejas de padres para cruzamiento \n",
" mating_pool = []\n",
" for i in range(int(popsize/2)): mating_pool.append(select_parents_roulette(population)) \n",
" \n",
" ## Crea la poblacion descendencia cruzando las parejas del mating pool con Recombinación de 1 punto\n",
" offspring_population = []\n",
" for i in range(len(mating_pool)): \n",
" #offspring_population.extend( mating_pool[i][0].crossover_onepoint(mating_pool[i][1]) )\n",
" offspring_population.extend( mating_pool[i][0].crossover_uniform(mating_pool[i][1]) )\n",
"\n",
" ## Aplica el operador de mutacion con probabilidad pmut en cada hijo generado\n",
" for i in range(len(offspring_population)):\n",
" if random.uniform(0, 1) < pmut: \n",
" offspring_population[i] = offspring_population[i].mutate_position()\n",
" \n",
" ## Evalua la poblacion descendencia\n",
" evaluate_population(offspring_population, fitness_fn) # evalua la poblacion inicial\n",
" \n",
" ## Selecciona popsize individuos para la sgte. generación de la union de la pob. actual y pob. descendencia\n",
" population = select_survivors(population, offspring_population, popsize)\n",
"\n",
" ## Almacena la historia del fitness del mejor individuo\n",
" ibest = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:1]\n",
" bestfitness.append(population[ibest[0]].fitness)\n",
" print(\"generacion {}, best_fitness = {}\".format(g, population[ibest[0]].fitness))\n",
" \n",
" return population[ibest[0]], bestfitness "
]
}
],
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"version": "3.9.2"
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|
