aima/search.py
def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1):
    "[Fig. 4.8]"
    for i in range(ngen):
        new_population = []
        for i in len(population):
            fitnesses = map(fitness_fn, population)
            p1, p2 = weighted_sample_with_replacement(population, fitnesses, 2)
            child = p1.mate(p2)
            if random.uniform(0, 1) < pmut:
                child.mutate()
            new_population.append(child)
        population = new_population
    return argmax(population, fitness_fn)
def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1):
    "[Fig. 4.8]"
    for i in range(ngen):
        new_population = []
        for i in len(population):
            fitnesses = map(fitness_fn, population)
            p1, p2 = weighted_sample_with_replacement(population, fitnesses, 2)
            child = p1.mate(p2)
            if random.uniform(0, 1) < pmut:
                child.mutate()
            new_population.append(child)
        population = new_population
    return argmax(population, fitness_fn)
def genetic_algorithm ( population , fitness_fn , ngen = 1000 , pmut = 0.1 ) : for i in range ( ngen ) : new_population = [ ] for i in len ( population ) : fitnesses = map ( fitness_fn , population ) p1 , p2 = weighted_sample_with_replacement ( population , fitnesses , 2 ) child = p1 . mate ( p2 ) if random . uniform ( 0 , 1 ) < pmut : child . mutate ( ) new_population . append ( child ) population = new_population return argmax ( population , fitness_fn )
[Fig. 4.8]
[ Fig . 4 . 8 ]