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   "source": [
    "# Prueba del algoritmo genetico"
   ]
  },
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   "id": "f6b4829a-9001-410c-b20c-01c65c777d8a",
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   "source": []
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   "execution_count": null,
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   "outputs": [],
   "source": []
  },
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   "execution_count": null,
   "id": "078280b5-70ef-4691-8798-a686d85d188c",
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   "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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