进化算法（四）微生物进化算法

"""Visualize Microbial Genetic Algorithm to find the maximum point in a graph.Visit my tutorial website for more: https://morvanzhou.github.io/tutorials/"""import numpy as npimport matplotlib.pyplot as pltDNA_SIZE = 10            # DNA lengthPOP_SIZE = 20            # population sizeCROSS_RATE = 0.6         # mating probability (DNA crossover)MUTATION_RATE = 0.01     # 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 functionclass MGA(object):    def __init__(self, DNA_size, DNA_bound, cross_rate, mutation_rate, pop_size):        self.DNA_size = DNA_size        DNA_bound[1] += 1        self.DNA_bound = DNA_bound        self.cross_rate = cross_rate        self.mutate_rate = mutation_rate        self.pop_size = pop_size        # initial DNAs for winner and loser        self.pop = np.random.randint(*DNA_bound, size=(1, self.DNA_size)).repeat(pop_size, axis=0)    def translateDNA(self, pop):        # convert binary DNA to decimal and normalize it to a range(0, 5)        return pop.dot(2 ** np.arange(self.DNA_size)[::-1]) / (2 ** self.DNA_size - 1) * X_BOUND[1]    def get_fitness(self, product):        return product      # it is OK to use product value as fitness in here    def crossover(self, loser_winner):      # crossover for loser        cross_idx = np.empty((self.DNA_size,)).astype(np.bool)        for i in range(self.DNA_size):            cross_idx[i] = True if np.random.rand() < self.cross_rate else False  # crossover index        loser_winner[0, cross_idx] = loser_winner[1, cross_idx]  # assign winners genes to loser        return loser_winner    def mutate(self, loser_winner):         # mutation for loser        mutation_idx = np.empty((self.DNA_size,)).astype(np.bool)        for i in range(self.DNA_size):            mutation_idx[i] = True if np.random.rand() < self.mutate_rate else False  # mutation index        # flip values in mutation points        loser_winner[0, mutation_idx] = ~loser_winner[0, mutation_idx].astype(np.bool)        return loser_winner    def evolve(self, n):    # nature selection wrt pop's fitness        for _ in range(n):  # random pick and compare n times            sub_pop_idx = np.random.choice(np.arange(0, self.pop_size), size=2, replace=False)            sub_pop = self.pop[sub_pop_idx]             # pick 2 from pop            product = F(self.translateDNA(sub_pop))            fitness = self.get_fitness(product)            loser_winner_idx = np.argsort(fitness)            loser_winner = sub_pop[loser_winner_idx]    # the first is loser and second is winner            loser_winner = self.crossover(loser_winner)            loser_winner = self.mutate(loser_winner)            self.pop[sub_pop_idx] = loser_winner        DNA_prod = self.translateDNA(self.pop)        pred = F(DNA_prod)        return DNA_prod, predplt.ion()       # something about plottingx = np.linspace(*X_BOUND, 200)plt.plot(x, F(x))ga = MGA(DNA_size=DNA_SIZE, DNA_bound=[0, 1], cross_rate=CROSS_RATE, mutation_rate=MUTATION_RATE, pop_size=POP_SIZE)for _ in range(N_GENERATIONS):                    # 100 generations    DNA_prod, pred = ga.evolve(5)          # natural selection, crossover and mutation    # something about plotting    if 'sca' in globals(): sca.remove()    sca = plt.scatter(DNA_prod, pred, s=200, lw=0, c='red', alpha=0.5); plt.pause(0.05)plt.ioff();plt.show()