遗传算法（一）

import numpy as npimport matplotlib.pyplot as pltDNA_SIZE = 10            # DNA lengthPOP_SIZE = 100           # population sizeCROSS_RATE = 0.8         # mating probability (DNA crossover)MUTATION_RATE = 0.003    # mutation probabilityN_GENERATIONS = 200X_BOUND = [0, 5]         # x upper and lower boundsdef F(x): return np.sin(10*x)*x + np.cos(2*x)*x     # to find the maximum of this function# find non-zero fitness for selectiondef get_fitness(pred): return pred + 1e-3 - np.min(pred)# convert binary DNA to decimal and normalize it to a range(0, 5)def translateDNA(pop): return pop.dot(2 ** np.arange(DNA_SIZE)[::-1]) / (2**DNA_SIZE-1) * X_BOUND[1]def select(pop, fitness):    # nature selection wrt pop's fitness    idx = np.random.choice(np.arange(POP_SIZE), size=POP_SIZE, replace=True,                           p=fitness/fitness.sum())    return pop[idx]def crossover(parent, pop):     # mating process (genes crossover)    if np.random.rand() < CROSS_RATE:        i_ = np.random.randint(0, POP_SIZE, size=1)                             # select another individual from pop        cross_points = np.random.randint(0, 2, size=DNA_SIZE).astype(np.bool)   # choose crossover points        parent[cross_points] = pop[i_, cross_points]                            # mating and produce one child    return parentdef mutate(child):    for point in range(DNA_SIZE):        if np.random.rand() < MUTATION_RATE:            child[point] = 1 if child[point] == 0 else 0    return childpop = np.random.randint(0, 2, (1, DNA_SIZE)).repeat(POP_SIZE, axis=0)  # initialize the pop DNAplt.ion()       # something about plottingx = np.linspace(*X_BOUND, 200)plt.plot(x, F(x))for _ in range(N_GENERATIONS):    F_values = F(translateDNA(pop))    # compute function value by extracting DNA    # something about plotting    if 'sca' in globals(): sca.remove()    sca = plt.scatter(translateDNA(pop), F_values, s=200, lw=0, c='red', alpha=0.5); plt.pause(0.05)    # GA part (evolution)    fitness = get_fitness(F_values)    print("Most fitted DNA: ", pop[np.argmax(fitness), :])    pop = select(pop, fitness)    pop_copy = pop.copy()    for parent in pop:        child = crossover(parent, pop_copy)        child = mutate(child)        parent[:] = child       # parent is replaced its childplt.ioff(); plt.show()