遗传算法

遗传算法：

先上代码：

# -*- coding: UTF-8 -*-
"""
Visualize Genetic Algorithm to match the target phrase.
Visit my tutorial website for more: https://morvanzhou.github.io/tutorials/
"""

import numpy as np

TARGET_PHRASE = 'You get it!'       # target DNA    最后目标
POP_SIZE = 300                      # population size   300 个祖宗
CROSS_RATE = 0.4                    # mating probability (DNA crossover)
MUTATION_RATE = 0.01                # mutation probability 变异
N_GENERATIONS = 1000   #进化代数

DNA_SIZE = len(TARGET_PHRASE)
TARGET_ASCII = np.fromstring(TARGET_PHRASE, dtype=np.uint8)  # convert string to number
ASCII_BOUND = [32, 126]   # ascii 范围


class GA(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

        # 生成第一代 300个祖宗 ，  数据类型 (300,11) =( pop_size, dna_size)  32  ~ 128 之间
        self.pop = np.random.randint(*DNA_bound, size=(pop_size, DNA_size)).astype(np.int8)  # int8 for convert to ASCII


    def translateDNA(self, DNA):                 # convert to readable string
        return DNA.tostring().decode('ascii')

    def get_fitness(self):                      # count how many character matches
        match_count = (self.pop == TARGET_ASCII).sum(axis=1)
        return match_count

    def select(self):  #排序生成输出， 有优势的 被选出概率更高
        fitness = self.get_fitness() + 1e-4     # add a small amount to avoid all zero fitness
        idx = np.random.choice(np.arange(self.pop_size), size=self.pop_size, replace=True, p=fitness/fitness.sum())
        '''
        data = fitness/fitness.sum()
        print data
        print idx

        print data.shape
        print idx.shape
        '''

        return self.pop[idx]

    def crossover(self, parent, pop):  #交叉生娃： 某一个男人，去随机选一个女人，交叉配对部分数据； 男人是否有权利靠 np.random.rand() < self.cross_rate
        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
            parent[cross_points] = pop[i_, cross_points]                            # mating and produce one child
            #print cross_points   50% 交叉
        return parent

    def mutate(self, child):  #小变异
        for point in range(self.DNA_size):
            if np.random.rand() < self.mutate_rate:
                child[point] = np.random.randint(*self.DNA_bound)  # choose a random ASCII index
        return child

    def evolve(self):  # 进化
        pop = self.select()

        #print pop.shape

        pop_copy = pop.copy()
        #print 'pop_copy:', pop_copy
        for parent in pop:  # for every parent
            child = self.crossover(parent, pop_copy)
            child = self.mutate(child)
            parent[:] = child
            #print 'child:',child
            #print 'parent:',parent
        self.pop = pop

if __name__ == '__main__':

    '''  300 个祖宗
    # np.random.randint(*DNA_bound, size=(pop_size, DNA_size)).astype(np.int8)
    data = np.random.randint(*ASCII_BOUND, size=(POP_SIZE, DNA_SIZE))
    print data   # (POP_SIZE, DNA_SIZE)).astype(np.int8)
    print data.shape

    data2 = data.astype(np.int8)
    print data2
    print data2.shape
    exit()
    '''

    ga = GA(DNA_size=DNA_SIZE, DNA_bound=ASCII_BOUND, cross_rate=CROSS_RATE,
            mutation_rate=MUTATION_RATE, pop_size=POP_SIZE)


    #ga.select( )

    '''
    print ga.get_fitness()
    eqlist = (ga.pop == TARGET_ASCII)
    print eqlist #.sum(axis=1)
    print eqlist.shape
    eqlistsum = eqlist.sum(axis=1)
    print eqlistsum #.sum(axis=1)
    print eqlistsum.shape
    exit()
    '''

    for generation in range(N_GENERATIONS):

        fitness = ga.get_fitness()
        best_DNA = ga.pop[ np.argmax(fitness)  ]
        best_phrase = ga.translateDNA(best_DNA)
        print('Gen', generation, ': ', best_phrase)
        if best_phrase == TARGET_PHRASE:
            break
        ga.evolve()

原理理解：