Fitness和DNA

• 0-1-2
• 0-2-1
• 1-0-2
• 1-2-0
• 2-0-1
• 2-1-0

`>>> np.random.permutation(3) # array([1, 2, 0])`

`fitness0 = 1/total_distance fitness1 = np.exp(1/total_distance)`

`if np.random.rand() < self.cross_rate:     i_ = np.random.randint(0, self.pop_size, size=1)                        # select another individual from pop     cross_points = np.random.randint(0, 2, self.DNA_size).astype(np.bool)   # choose crossover points     keep_city = parent[cross_points]                                       # find the city number     swap_city = pop[i_, np.isin(pop[i_].ravel(), keep_city, invert=True)]   # 找到与爸爸不同的城市     parent[:] = np.concatenate((keep_city, swap_city))`

`for point in range(self.DNA_size):     if np.random.rand() < self.mutate_rate:         swap_point = np.random.randint(0, self.DNA_size)         swapA, swapB = child[point], child[swap_point]         child[point], child[swap_point] = swapB, swapA`

`""" Visualize Genetic Algorithm to find the shortest path for travel sales problem. Visit my tutorial website for more: https://morvanzhou.github.io/tutorials/ """ import matplotlib.pyplot as plt import numpy as np  N_CITIES = 20  # DNA size CROSS_RATE = 0.1 MUTATE_RATE = 0.02 POP_SIZE = 500 N_GENERATIONS = 500   class GA(object):     def __init__(self, DNA_size, cross_rate, mutation_rate, pop_size, ):         self.DNA_size = DNA_size         self.cross_rate = cross_rate         self.mutate_rate = mutation_rate         self.pop_size = pop_size          self.pop = np.vstack([np.random.permutation(DNA_size) for _ in range(pop_size)])      def translateDNA(self, DNA, city_position):     # get cities' coord in order         line_x = np.empty_like(DNA, dtype=np.float64)         line_y = np.empty_like(DNA, dtype=np.float64)         for i, d in enumerate(DNA):             city_coord = city_position[d]             line_x[i, :] = city_coord[:, 0]             line_y[i, :] = city_coord[:, 1]         return line_x, line_y      def get_fitness(self, line_x, line_y):         total_distance = np.empty((line_x.shape[0],), dtype=np.float64)         for i, (xs, ys) in enumerate(zip(line_x, line_y)):             total_distance[i] = np.sum(np.sqrt(np.square(np.diff(xs)) + np.square(np.diff(ys))))         fitness = np.exp(self.DNA_size * 2 / total_distance)         return fitness, total_distance      def select(self, fitness):         idx = np.random.choice(np.arange(self.pop_size), size=self.pop_size, replace=True, p=fitness / fitness.sum())         return self.pop[idx]      def crossover(self, parent, pop):         if np.random.rand() < self.cross_rate:             i_ = np.random.randint(0, self.pop_size, size=1)                        # select another individual from pop             cross_points = np.random.randint(0, 2, self.DNA_size).astype(np.bool)   # choose crossover points             keep_city = parent[~cross_points]                                       # find the city number             swap_city = pop[i_, np.isin(pop[i_].ravel(), keep_city, invert=True)]             parent[:] = np.concatenate((keep_city, swap_city))         return parent      def mutate(self, child):         for point in range(self.DNA_size):             if np.random.rand() < self.mutate_rate:                 swap_point = np.random.randint(0, self.DNA_size)                 swapA, swapB = child[point], child[swap_point]                 child[point], child[swap_point] = swapB, swapA         return child      def evolve(self, fitness):         pop = self.select(fitness)         pop_copy = pop.copy()         for parent in pop:  # for every parent             child = self.crossover(parent, pop_copy)             child = self.mutate(child)             parent[:] = child         self.pop = pop   class TravelSalesPerson(object):     def __init__(self, n_cities):         self.city_position = np.random.rand(n_cities, 2)         plt.ion()      def plotting(self, lx, ly, total_d):         plt.cla()         plt.scatter(self.city_position[:, 0].T, self.city_position[:, 1].T, s=100, c='k')         plt.plot(lx.T, ly.T, 'r-')         plt.text(-0.05, -0.05, "Total distance=%.2f" % total_d, fontdict={'size': 20, 'color': 'red'})         plt.xlim((-0.1, 1.1))         plt.ylim((-0.1, 1.1))         plt.pause(0.01)   ga = GA(DNA_size=N_CITIES, cross_rate=CROSS_RATE, mutation_rate=MUTATE_RATE, pop_size=POP_SIZE)  env = TravelSalesPerson(N_CITIES) for generation in range(N_GENERATIONS):     lx, ly = ga.translateDNA(ga.pop, env.city_position)     fitness, total_distance = ga.get_fitness(lx, ly)     ga.evolve(fitness)     best_idx = np.argmax(fitness)     print('Gen:', generation, '| best fit: %.2f' % fitness[best_idx],)      env.plotting(lx[best_idx], ly[best_idx], total_distance[best_idx])  plt.ioff() plt.show()`