今天整理文件的时候发现桌面上有一个 yichuan.py的文件，打开看了一下，应该是以前学习的时候写的python下的遗传算法。记录一下吧
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline
# 找到函数f(x)在区间self.x_bounder上的最大值
def f(x):
return np.sin(x) + np.cos(x)
class GeneticAlgorithm(object):
"""遗传算法.
Parameters:
-----------
cross_rate: float
交配的可能性大小.
mutate_rate: float
基因突变的可能性大小.
n_population: int
种群的大小.
n_iterations: int
迭代次数.
DNA_size: int
DNA的长度.
x_bounder: list
x 轴的区间, 用遗传算法寻找x在该区间中的最大值.
"""
def __init__(self, cross_rate, mutation_rate, n_population, n_iterations, DNA_size):
self.cross_rate = cross_rate
self.mutate_rate = mutation_rate
self.n_population = n_population
self.n_iterations = n_iterations
self.DNA_size = 8 # DNA的长度
self.x_bounder = [-3, 4]
# 初始化一个种群
def init_population(self):
population = np.random.randint(low=0, high=2, size=(self.n_population, self.DNA_size)).astype(np.int8)
return population
# 将种群中的每个个体的DNA由二进制转换成十进制
def transformDNA(self, population):
population_decimal = ( (population.dot(np.power(2, np.arange(self.DNA_size)[::-1])) / np.power(2, self.DNA_size) - 0.5) *
(self.x_bounder[1] - self.x_bounder[0]) + 0.5 * (self.x_bounder[0] + self.x_bounder[1]) )
return population_decimal
# 计算种群中每个个体的适应度，适应度越高，说明该个体的基因越好
def fitness(self, population):
transform_population = self.transformDNA(population)
fitness_score = f(transform_population)
return fitness_score - fitness_score.min() # 在select函数中按照个体的适应度进行抽样的的时候，抽样概率值必须是非负的
# 对种群按照其适应度进行采样，这样适应度高的个体就会以更高的概率被选择
def select(self, population, fitness_score):
fitness_score = fitness_score + 1e-4 # 下一步抽样的过程中用到了除法，出现除法就要考虑到分母为0的特殊情况
idx = np.random.choice(np.arange(self.n_population), size=self.n_population, replace=True, p=fitness_score/fitness_score.sum())
return population[idx]
# 进行交配
def create_child(self, parent, pop):
if np.random.rand() < self.cross_rate:
index = np.random.randint(0, self.n_population, size=1)
cross_points = np.random.randint(0, 2, self.DNA_size).astype(np.bool)
parent[cross_points] = pop[index, cross_points]
return parent
# 基因突变
def mutate_child(self, child):
for i in range(self.DNA_size):
if np.random.rand() < self.mutate_rate:
child[i] = 1
else:
child[i] = 0
return child
# 进化
def evolution(self):
population = self.init_population()
for i in range(self.n_iterations):
fitness_score = self.fitness(population)
best_person = population[np.argmax(fitness_score)]
if i%100 == 0:
print(u'第%-4d次进化后, 基因(fitness_score)最好的个体是: %s, 其适应度(找到的函数最大值)是: %f' % (i, best_person,
f(self.transformDNA(best_person)) ) )
population = self.select(population, fitness_score)
population_copy = population.copy()
for parent in population:
child = self.create_child(parent, population_copy)
child = self.mutate_child(child)
parent[:] = child
population = population
self.best_person = best_person
def main():
ga = GeneticAlgorithm(cross_rate=0.9, mutation_rate=0.1, n_population=300, n_iterations=2000, DNA_size=8)
ga.evolution()
# 绘图
x = np.linspace(start=ga.x_bounder[0], stop=ga.x_bounder[1], num=200)
plt.plot(x, f(x))
plt.scatter(ga.transformDNA(ga.best_person), f(ga.transformDNA(ga.best_person)), s=200, lw=0, c='red', alpha=0.5)
ax = plt.gca()
ax.spines['right'].set_color('none') # 去掉右侧的轴
ax.spines['top'].set_color('none') # 去掉上方的轴
ax.xaxis.set_ticks_position('bottom') # 设置x轴的刻度仅在下方显示
ax.yaxis.set_ticks_position('left') # 设置y轴的刻度仅在左边显示
plt.show()
if __name__ == '__main__':
main()
应该是能用的，因为在忙着整理文件，未测试。