algogenetico.py

import random
  
modelo = [[1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[37,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1],
[0,264,0,265,0,266,0,267,0,268,0,269,0,270,0,271,0,272,0,273,0,274,0,275,0,276,0,277,0,278,0,279,0,280,0,281,0,282,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,112,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1],
[0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,112,1,0,0,0,0,0,1,0,1,0,1,0,1,0,1,0,1],
[0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,112,1,0,1,1,1,0,1,0,1,0,1,0,1,0,1,0,1],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,189,0,190,190,190,0,0,0,0,0,0,0,0,0,0,0,0],
[0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,1,1,123,1,0,1,0,1,0,1,0,1,0,1],
[0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,0,0,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,0,0,123,1,0,1,0,1,0,1,0,1,0,1],
[0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,123,1,0,1,0,1,0,1,0,1,0,1],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,225,0,0,0,0,0,0,0,0,0,0],
[0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1],
[0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1]] #Objetivo a alcanzar
largo = 10 #La longitud del material genetico de cada individuo
num = 10 #La cantidad de individuos que habra en la poblacion
pressure = 22 #Cuantos individuos se seleccionan para reproduccion. Necesariamente mayor que 2
mutation_chance = 0.2 #La probabilidad de que un individuo mute
  
print("\n\nModelo: %s\n"%(modelo)) #Mostrar el modelo, con un poco de espaciado
  
def individual(min, max):
    """
        Crea un individual
    """
    return[random.randint(min, max) for i in range(largo)]
  
def crearPoblacion():
    """
        Crea una poblacion nueva de individuos
    """
    return [individual(1,9) for i in range(num)]
  
def calcularFitness(individual):
    """
        Calcula el fitness de un individuo concreto.
    """
    fitness = 0
    for i in range(len(individual)):
        if individual[i] == modelo[i]:
            fitness += 1
  
    return fitness
  
def selection_and_reproduction(population):
    """
        Puntua todos los elementos de la poblacion (population) y se queda con los mejores
        guardandolos dentro de 'selected'.
        Despues mezcla el material genetico de los elegidos para crear nuevos individuos y
        llenar la poblacion (guardando tambien una copia de los individuos seleccionados sin
        modificar).
  
        Por ultimo muta a los individuos.
  
    """
    puntuados = [ (calcularFitness(i), i) for i in population] #Calcula el fitness de cada individuo, y lo guarda en pares ordenados de la forma (5 , [1,2,1,1,4,1,8,9,4,1])
    puntuados = [i[1] for i in sorted(puntuados)] #Ordena los pares ordenados y se queda solo con el array de valores
    population = puntuados
  
  
  
    selected =  puntuados[(len(puntuados)-pressure):] #Esta linea selecciona los 'n' individuos del final, donde n viene dado por 'pressure'
  
  
  
    #Se mezcla el material genetico para crear nuevos individuos
    for i in range(len(population)-pressure):
        punto = random.randint(1,largo-1) #Se elige un punto para hacer el intercambio
        padre = random.sample(selected, 2) #Se eligen dos padres
          
        population[i][:punto] = padre[0][:punto] #Se mezcla el material genetico de los padres en cada nuevo individuo
        population[i][punto:] = padre[1][punto:]
  
    return population #El array 'population' tiene ahora una nueva poblacion de individuos, que se devuelven
  
def mutation(population):
    """
        Se mutan los individuos al azar. Sin la mutacion de nuevos genes nunca podria
        alcanzarse la solucion.
    """
    for i in range(len(population)-pressure):
        if random.random() <= mutation_chance: #Cada individuo de la poblacion (menos los padres) tienen una probabilidad de mutar
            punto = random.randint(0,largo-1) #Se elgie un punto al azar
            nuevo_valor = random.randint(1,9) #y un nuevo valor para este punto
  
            #Es importante mirar que el nuevo valor no sea igual al viejo
            while nuevo_valor == population[i][punto]:
                nuevo_valor = random.randint(1,9)
  
            #Se aplica la mutacion
            population[i][punto] = nuevo_valor
  
    return population
      
  
  
population = crearPoblacion()#Inicializar una poblacion
print("Poblacion Inicial:\n%s"%(population)) #Se muestra la poblacion inicial
  
  
#Se evoluciona la poblacion
for i in range(100):
    population = selection_and_reproduction(population)
    population = mutation(population)
  
  
print("\nPoblacion Final:\n%s"%(population)) #Se muestra la poblacion evolucionada
print("\n\n")

settings.json

{
    "python.pythonPath": "D:\\Users\\ximec\\AppData\\Local\\Programs\\python.exe"
}