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-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# APLICACIONES EN CIENCIAS DE COMPUTACION\n",
- "Dr. Edwin Villanueva"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Algoritmo genetico para resolver el VRPTW\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 140,
- "metadata": {},
- "outputs": [],
- "source": [
- "import random\n",
- "import matplotlib.pyplot as plt\n",
- "import csv"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<b>Clase abstracta de un individuo de algoritmo genético</b>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "class Individual:\n",
- " \"Clase abstracta para individuos de un algoritmo evolutivo.\"\n",
- "\n",
- " def __init__(self, chromosome):\n",
- " self.chromosome = chromosome\n",
- "\n",
- " def crossover(self, other):\n",
- " \"Retorna un nuevo individuo cruzando self y other.\"\n",
- " raise NotImplementedError\n",
- " \n",
- " def mutate(self):\n",
- " \"Cambia los valores de algunos genes.\"\n",
- " raise NotImplementedError "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<b>Clase concreta de un individuo del problema de las n-reinas</b>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 105,
- "metadata": {},
- "outputs": [],
- "source": [
- "class Individual_VRPTW(Individual):\n",
- " \"Clase que implementa el individuo en VRPTW.\"\n",
- "\n",
- " def __init__(self, chromosome):\n",
- " self.chromosome = chromosome[:]\n",
- " self.fitness = -1\n",
- " \n",
- " def crossover_order(self, other):\n",
- " \"\"\"\n",
- " Copies a part of the child chromosome from the first parent and constructs \n",
- " the remaining part by following the vertex ordering in the second parent\n",
- " \"\"\"\n",
- " cut_point1 = random.randrange(0, len(self.chromosome) + 1)\n",
- " cut_point2 = random.randrange(cut_point1, len(self.chromosome) + 1)\n",
- " \n",
- " c1 = self.chromosome[:]\n",
- " c2 = other.chromosome[:]\n",
- " p1_rem = self.chromosome[:cut_point1] + self.chromosome[cut_point2:]\n",
- " p2_rem = other.chromosome[:cut_point1] + other.chromosome[cut_point2:]\n",
- " # Change the genes in the remaining part of the child...\n",
- " for i in range(len(self.chromosome)):\n",
- " if i not in range(cut_point1, cut_point2):\n",
- " # ...following the vertex ordering in the second parent\n",
- " for gene in other.chromosome:\n",
- " if gene in p1_rem:\n",
- " c1.chromosome[i] = gene\n",
- " \n",
- " # (now for the other child)\n",
- " for gene in self.chromosome:\n",
- " if gene in p2_rem:\n",
- " c2.chromosome[i] = gene\n",
- " \n",
- " return [Individual_VRPTW(c1), Individual_VRPTW(c2)]\n",
- " \n",
- "\n",
- " def crossover_onepoint(self, other):\n",
- " \"Retorna dos nuevos individuos del cruzamiento de un punto entre self y other \"\n",
- " c = random.randrange(len(self.chromosome))\n",
- " ind1 = Individual_VRPTW(self.chromosome[:c] + other.chromosome[c:])\n",
- " ind2 = Individual_VRPTW(other.chromosome[:c] + self.chromosome[c:])\n",
- " return [ind1, ind2] \n",
- " \n",
- " def crossover_uniform(self, other):\n",
- " chromosome1 = []\n",
- " chromosome2 = []\n",
- " \"Retorna dos nuevos individuos del cruzamiento uniforme entre self y other \"\n",
- " for i in range(len(self.chromosome)):\n",
- " if random.uniform(0, 1) < 0.5:\n",
- " chromosome1.append(self.chromosome[i])\n",
- " chromosome2.append(other.chromosome[i])\n",
- " else:\n",
- " chromosome1.append(other.chromosome[i])\n",
- " chromosome2.append(self.chromosome[i])\n",
- " ind1 = Individual_VRPTW(chromosome1)\n",
- " ind2 = Individual_VRPTW(chromosome2)\n",
- " return [ind1, ind2] \n",
- "\n",
- " def mutate_position(self):\n",
- " mutated_ind = Individual_VRPTW(self.chromosome[:])\n",
- " indexPos = random.randint(0, len(mutated_ind.chromosome)-1)\n",
- " newPos = random.randint(0, len(mutated_ind.chromosome)-1)\n",
- " mutated_ind.chromosome[indexPos] = newPos\n",
- " return mutated_ind\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<b>Funcion de fitness para evaluar un individuo del problema de las n-reinas</b>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 109,
- "metadata": {},
- "outputs": [],
- "source": [
- "def fitness_VRPTW(chromosome):\n",
- " \"\"\"Retorna el fitness de un cromosoma en el problema VRPTW (distancia total de todas las rutas) \"\"\"\n",
- " n = len(chromosome) # No. of vertices\n",
- " fitness = 10**6\n",
- " # feasibility\n",
- " # TODO: considerar todas las restricciones\n",
- " # desirability\n",
- " for i in range(0, n):\n",
- " fitness -= distancia[i][i + 1]\n",
- " \n",
- " return fitness"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<b>Funcion para evaluar toda una población de individuos con la funcion de fitnes especificada</b>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "def evaluate_population(population, fitness_fn):\n",
- " \"\"\" Evalua una poblacion de individuos con la funcion de fitness pasada \"\"\"\n",
- " popsize = len(population)\n",
- " for i in range(popsize):\n",
- " if population[i].fitness == -1: # si el individuo no esta evaluado\n",
- " population[i].fitness = fitness_fn(population[i].chromosome)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<b>Funcion que selecciona con el metodo de la ruleta un par de individuos de population para cruzamiento </b>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 103,
- "metadata": {},
- "outputs": [],
- "source": [
- "def select_parents_roulette(population):\n",
- " popsize = len(population)\n",
- " \n",
- " # Escoje el primer padre\n",
- " sumfitness = sum([indiv.fitness for indiv in population]) # suma total del fitness de la poblacion\n",
- " pickfitness = random.uniform(0, sumfitness) # escoge un numero aleatorio entre 0 y sumfitness\n",
- " cumfitness = 0 # fitness acumulado\n",
- " for i in range(popsize):\n",
- " cumfitness += population[i].fitness\n",
- " if cumfitness > pickfitness: \n",
- " iParent1 = i\n",
- " break\n",
- " \n",
- " # Escoje el segundo padre, desconsiderando el padre ya escogido\n",
- " sumfitness = sumfitness - population[iParent1].fitness # retira el fitness del padre ya escogido\n",
- " pickfitness = random.uniform(0, sumfitness) # escoge un numero aleatorio entre 0 y sumfitness\n",
- " cumfitness = 0 # fitness acumulado\n",
- " for i in range(popsize):\n",
- " if i == iParent1: continue # si es el primer padre \n",
- " cumfitness += population[i].fitness\n",
- " if cumfitness > pickfitness: \n",
- " iParent2 = i\n",
- " break \n",
- " return (population[iParent1], population[iParent2])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<b>Funcion que selecciona sobrevivientes para la sgte generacion, dada la poblacion actual y poblacion de hijos </b>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "def select_survivors(population, offspring_population, numsurvivors):\n",
- " next_population = []\n",
- " population.extend(offspring_population) # une las dos poblaciones\n",
- " isurvivors = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:numsurvivors]\n",
- " for i in range(numsurvivors): next_population.append(population[isurvivors[i]])\n",
- " return next_population"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<b>Algoritmo Genetico</b> \n",
- "Recibe una poblacion inicial, funcion de fitness, numero de generaciones (ngen) y taza de mutación (pmut)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "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",
- " offspring_population.extend( mating_pool[i][0].crossover_order(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 "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " <b>Algoritmo de Busqueda Genetica para el VRPTW</b> "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 117,
- "metadata": {},
- "outputs": [],
- "source": [
- "def genetic_algorithm_VRPTW(fitness_fn, num_depots=1, num_vehicles=1, vehicle_capacity=200, popsize=10, ngen=1000, pmut=0):\n",
- " population = []\n",
- " \n",
- " # Crea la poblacion inicial con cromosomas aleatorios\n",
- " for i in range(popsize):\n",
- " chromosome = [j for j in range(1,num_vertices+1)]\n",
- " random.shuffle(chromosome)\n",
- " population.append(Individual_VRPTW(chromosome))\n",
- " \n",
- " return genetic_algorithm(population, fitness_fn, ngen, pmut)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "def genetic_search_nqueens(fitness_fn, num_queens=10, popsize=10, ngen=100, pmut=0.5):\n",
- " import random\n",
- " population = []\n",
- "\n",
- " ## Crea la poblacion inicial con cromosomas aleatorios\n",
- " for i in range(popsize):\n",
- " chromosome = [j for j in range(1,num_queens+1)]\n",
- " random.shuffle(chromosome)\n",
- " population.append( Individual_nqueens(chromosome) )\n",
- " \n",
- " ## Crea la poblacion inicial con los siguientes cromosomas \n",
- " #chromosomes = [[1,3,1,3,1,3,1,3,1,3],\n",
- " # [2,4,2,4,2,4,2,4,2,4],\n",
- " # [3,5,3,5,3,5,3,5,3,5],\n",
- " # [4,6,4,6,4,6,4,6,4,6],\n",
- " # [5,7,5,7,5,7,5,7,5,7],\n",
- " # [6,8,6,8,6,8,6,8,6,8],\n",
- " # [7,9,7,9,7,9,7,9,7,9],\n",
- " # [8,10,8,10,8,10,8,10,8,10],\n",
- " # [9,1,9,1,9,1,9,1,9,1],\n",
- " # [10,2,10,2,10,2,10,2,10,2] ] \n",
- " #for i in range(popsize):\n",
- " # population.append( Individual_nqueens(chromosomes[i]) ) \n",
- " \n",
- " ## llama al algoritmo genetico para encontrar una solucion al problema de las n reinas\n",
- " return genetic_algorithm(population, fitness_fn, ngen, pmut)\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Probando el Algoritmo genetico"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 115,
- "metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'distancia' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Input \u001b[0;32mIn [115]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# busca solucion para el problema de 10 reinas. Usa 100 individuos aleatorios, 100 generaciones y taza de mutación de 0.5\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m best_ind, bestfitness \u001b[38;5;241m=\u001b[39m \u001b[43mgenetic_algorithm_VRPTW\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfitness_VRPTW\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(bestfitness)\n\u001b[1;32m 6\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
- "Input \u001b[0;32mIn [113]\u001b[0m, in \u001b[0;36mgenetic_algorithm_VRPTW\u001b[0;34m(fitness_fn, num_vertices, num_depots, num_vehicles, popsize, ngen, pmut)\u001b[0m\n\u001b[1;32m 7\u001b[0m random\u001b[38;5;241m.\u001b[39mshuffle(chromosome)\n\u001b[1;32m 8\u001b[0m population\u001b[38;5;241m.\u001b[39mappend(Individual_VRPTW(chromosome))\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgenetic_algorithm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfitness_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mngen\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpmut\u001b[49m\u001b[43m)\u001b[49m\n",
- "Input \u001b[0;32mIn [10]\u001b[0m, in \u001b[0;36mgenetic_algorithm\u001b[0;34m(population, fitness_fn, ngen, pmut)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAlgoritmo Genetico \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4\u001b[0m popsize \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(population)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mevaluate_population\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfitness_fn\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# evalua la poblacion inicial\u001b[39;00m\n\u001b[1;32m 6\u001b[0m ibest \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(population)), key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m i: population[i]\u001b[38;5;241m.\u001b[39mfitness, reverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)[:\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 7\u001b[0m bestfitness \u001b[38;5;241m=\u001b[39m [population[ibest[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39mfitness]\n",
- "Input \u001b[0;32mIn [7]\u001b[0m, in \u001b[0;36mevaluate_population\u001b[0;34m(population, fitness_fn)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(popsize):\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m population[i]\u001b[38;5;241m.\u001b[39mfitness \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m: \u001b[38;5;66;03m# si el individuo no esta evaluado\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m population[i]\u001b[38;5;241m.\u001b[39mfitness \u001b[38;5;241m=\u001b[39m \u001b[43mfitness_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchromosome\u001b[49m\u001b[43m)\u001b[49m\n",
- "Input \u001b[0;32mIn [109]\u001b[0m, in \u001b[0;36mfitness_VRPTW\u001b[0;34m(chromosome)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# feasibility\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# TODO: considerar todas las restricciones\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# desirability\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m, n):\n\u001b[0;32m----> 9\u001b[0m fitness \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mdistancia\u001b[49m[i][i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fitness\n",
- "\u001b[0;31mNameError\u001b[0m: name 'distancia' is not defined"
- ]
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "best_ind, bestfitness = genetic_algorithm_VRPTW(fitness_VRPTW)\n",
- "plt.plot(bestfitness)\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 141,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Poblacion inicial, best_fitness = 44\n",
- "generacion 0, best_fitness = 44\n",
- "generacion 1, best_fitness = 44\n",
- "generacion 2, best_fitness = 44\n",
- "generacion 3, best_fitness = 44\n",
- "generacion 4, best_fitness = 44\n",
- "generacion 5, best_fitness = 44\n",
- "generacion 6, best_fitness = 44\n",
- "generacion 7, best_fitness = 44\n",
- "generacion 8, best_fitness = 44\n",
- "generacion 9, best_fitness = 44\n",
- "generacion 10, best_fitness = 44\n",
- "generacion 11, best_fitness = 44\n",
- "generacion 12, best_fitness = 44\n",
- "generacion 13, best_fitness = 44\n",
- "generacion 14, best_fitness = 44\n",
- "generacion 15, best_fitness = 44\n",
- "generacion 16, best_fitness = 44\n",
- "generacion 17, best_fitness = 44\n",
- "generacion 18, best_fitness = 44\n",
- "generacion 19, best_fitness = 44\n",
- "generacion 20, best_fitness = 44\n",
- "generacion 21, best_fitness = 44\n",
- "generacion 22, best_fitness = 44\n",
- "generacion 23, best_fitness = 44\n",
- "generacion 24, best_fitness = 44\n",
- "generacion 25, best_fitness = 44\n",
- "generacion 26, best_fitness = 44\n",
- "generacion 27, best_fitness = 44\n",
- "generacion 28, best_fitness = 44\n",
- "generacion 29, best_fitness = 44\n",
- "generacion 30, best_fitness = 45\n",
- "generacion 31, best_fitness = 45\n",
- "generacion 32, best_fitness = 45\n",
- "generacion 33, best_fitness = 45\n",
- "generacion 34, best_fitness = 45\n",
- "generacion 35, best_fitness = 45\n",
- "generacion 36, best_fitness = 45\n",
- "generacion 37, best_fitness = 45\n",
- "generacion 38, best_fitness = 45\n",
- "generacion 39, best_fitness = 45\n",
- "generacion 40, best_fitness = 45\n",
- "generacion 41, best_fitness = 45\n",
- "generacion 42, best_fitness = 45\n",
- "generacion 43, best_fitness = 45\n",
- "generacion 44, best_fitness = 45\n",
- "generacion 45, best_fitness = 45\n",
- "generacion 46, best_fitness = 45\n",
- "generacion 47, best_fitness = 45\n",
- "generacion 48, best_fitness = 45\n",
- "generacion 49, best_fitness = 45\n",
- "generacion 50, best_fitness = 45\n",
- "generacion 51, best_fitness = 45\n",
- "generacion 52, best_fitness = 45\n",
- "generacion 53, best_fitness = 45\n",
- "generacion 54, best_fitness = 45\n",
- "generacion 55, best_fitness = 45\n",
- "generacion 56, best_fitness = 45\n",
- "generacion 57, best_fitness = 45\n",
- "generacion 58, best_fitness = 45\n",
- "generacion 59, best_fitness = 45\n",
- "generacion 60, best_fitness = 45\n",
- "generacion 61, best_fitness = 45\n",
- "generacion 62, best_fitness = 45\n",
- "generacion 63, best_fitness = 45\n",
- "generacion 64, best_fitness = 45\n",
- "generacion 65, best_fitness = 45\n",
- "generacion 66, best_fitness = 45\n",
- "generacion 67, best_fitness = 45\n",
- "generacion 68, best_fitness = 45\n",
- "generacion 69, best_fitness = 45\n",
- "generacion 70, best_fitness = 45\n",
- "generacion 71, best_fitness = 45\n",
- "generacion 72, best_fitness = 45\n",
- "generacion 73, best_fitness = 45\n",
- "generacion 74, best_fitness = 45\n",
- "generacion 75, best_fitness = 45\n",
- "generacion 76, best_fitness = 45\n",
- "generacion 77, best_fitness = 45\n",
- "generacion 78, best_fitness = 45\n",
- "generacion 79, best_fitness = 45\n",
- "generacion 80, best_fitness = 45\n",
- "generacion 81, best_fitness = 45\n",
- "generacion 82, best_fitness = 45\n",
- "generacion 83, best_fitness = 45\n",
- "generacion 84, best_fitness = 45\n",
- "generacion 85, best_fitness = 45\n",
- "generacion 86, best_fitness = 45\n",
- "generacion 87, best_fitness = 45\n",
- "generacion 88, best_fitness = 45\n",
- "generacion 89, best_fitness = 45\n",
- "generacion 90, best_fitness = 45\n",
- "generacion 91, best_fitness = 45\n",
- "generacion 92, best_fitness = 45\n",
- "generacion 93, best_fitness = 45\n",
- "generacion 94, best_fitness = 45\n",
- "generacion 95, best_fitness = 45\n",
- "generacion 96, best_fitness = 45\n",
- "generacion 97, best_fitness = 45\n",
- "generacion 98, best_fitness = 45\n",
- "generacion 99, best_fitness = 45\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 343 ms, sys: 108 ms, total: 450 ms\n",
- "Wall time: 329 ms\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "# busca solucion para el problema de 10 reinas. Usa 100 individuos aleatorios, 100 generaciones y taza de mutación de 0.5\n",
- "best_ind, bestfitness = genetic_search_nqueens(fitness_nqueens, 10, 100, 100, 0.90)\n",
- "plt.plot(bestfitness)\n",
- "plt.show()"
- ]
- },
- {
- "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": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Lectura de datos"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 139,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CUST NO.XCOORD. YCOORD. DEMAND READY TIME DUE DATE SERVICE TIME\n",
- "1 40 50 0 0 240 0 \n",
- "2 25 85 20 145 175 10 \n",
- "3 22 75 30 50 80 10 \n",
- "4 22 85 10 109 139 10 \n",
- "5 20 80 40 141 171 10 \n",
- "6 20 85 20 41 71 10 \n",
- "7 18 75 20 95 125 10 \n",
- "8 15 75 20 79 109 10 \n",
- "9 15 80 10 91 121 10 \n",
- "10 10 35 20 91 121 10 \n",
- "11 10 40 30 119 149 10 \n",
- "12 8 40 40 59 89 10 \n",
- "13 8 45 20 64 94 10 \n",
- "14 5 35 10 142 172 10 \n",
- "15 5 45 10 35 65 10 \n",
- "16 2 40 20 58 88 10 \n",
- "17 0 40 20 72 102 10 \n",
- "18 0 45 20 149 179 10 \n",
- "19 44 5 20 87 117 10 \n",
- "20 42 10 40 72 102 10 \n",
- "21 42 15 10 122 152 10 \n",
- "22 40 5 10 67 97 10 \n",
- "23 40 15 40 92 122 10 \n",
- "24 38 5 30 65 95 10 \n",
- "25 38 15 10 148 178 10 \n",
- "26 35 5 20 154 184 10 \n",
- "27 95 30 30 115 145 10 \n",
- "28 95 35 20 62 92 10 \n",
- "29 92 30 10 62 92 10 \n",
- "30 90 35 10 67 97 10 \n",
- "31 88 30 10 74 104 10 \n",
- "32 88 35 20 61 91 10 \n",
- "33 87 30 10 131 161 10 \n",
- "34 85 25 10 51 81 10 \n",
- "35 85 35 30 111 141 10 \n",
- "36 67 85 20 139 169 10 \n",
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- ]
- }
- ],
- "source": [
- "with open('data/RC101.csv', newline='') as csvfile:\n",
- " orders = csv.reader(csvfile)\n",
- " for row in orders:\n",
- " print(f\"{row[0]:8}{row[1]:8}{row[2]:8}{row[3]:8}{row[4]:12}{row[5]:12}{row[6]:12}\")\n",
- " #print(\", \".join(row))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.9.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
generated by cgit v1.2.3 (git 2.25.1) at 2025-10-28 23:06:07 +0000