Python遗传算法求一元函数最大值 遗传算法求一元函数最大值 Python遗传算法求一元函数最大值 前言前言 遗传算法求一元函数最大值前言代码后记参考文献 最近接触遗传算法，参考了众多例子，有些又不尽然对，所以自己边理解边修改，然后写出了下面这堆传说中的屎山。。。 最近接触遗传算法，参考了众多例子，有些又不尽然对，所以自己边理解边修改，然后写出了下面这堆传说中的屎山。。。 ：遗传算法原理啥的太多了，就不赘述了，CSDN里面很多帖子都讲得很透彻了； PS1：遗传算法原理啥的太多了，就不赘述了， 里面很多帖子都讲得很透彻了； PS2：要看简洁的，直接油管搜遗传算法，看莫烦的视频。 ：要看简洁的，直接油管搜遗传算法，看莫烦的视频。 代码代码 不废话了，赶紧上车，啊不，上代码。 不废话了，赶紧上车，啊不，上代码。 import math import numpy as np import matplotlib.pyplot as plt import random class GA(object): # 目标求解2*sin(x)+cos(x)最大值 def __init__(self, population_size, chromosome_length, pm): self.population_size = population_size self.chromosome_length = chromosome_length self.pm = pm # 初始化种群 def species_origin(self): # 生成染色体和基因 population = [[]] # 二维列表，包含染色体和基因 for i in range(self.population_size): temporary = [] # 染色体寄存器 for j in range(self.chromosome_length): temporary.append(random.randint(0, 1)) # 生成基因，二进制 population.append(temporary) # 将基因添加到染色体中 return population[1:] # 返回种群，包含染色体和基因 # 种群解码，用于适应度判断 def translation(self, population): # 二进制转十进制 chromosome_d_list = [] # 创建染色体十进制列表 for i in range(len(population)): chromosome_value_d = 0 for j in range(len(population[0])): chromosome_value_d += population[i][j] * (math.pow(2, len(population[0])-j-1)) chromosome_d_list.append(chromosome_value_d) return chromosome_d_list # 适应度计算 def function(self, population, lower_bound, upper_bound): all_fitness_list = [] # 创建所有染色体适应度列表 fv_list = self.translation(population) for i in range(len(fv_list)): x = lower_bound + fv_list[i] * (upper_bound - lower_bound)/(math.pow(2, self.chromosome_length) - 1) y = 2 * math.sin(x) + math.cos(x) # 目标函数 all_fitness_list.append(y) return all_fitness_list # 剔除适应度负值 def positive_fitness(self, all_fitness_list): pf = [] for i in range(len(all_fitness_list)): if all_fitness_list[i] > 0: pf.append(all_fitness_list[i]) return pf # 正适应度染色体列表 def positive_chromosome(self, all_fitness_list, population): positive_chromosome_list = [] for i in range(len(all_fitness_list)): if all_fitness_list[i] > 0: positive_chromosome_list.append(population[i]) return positive_chromosome_list # 计算正适应度总和 

def pf_sum(self, pf): pf_total = 0 for i in range(len(pf)): pf_total += pf[i] return pf_total # 正适应度变为小数 def pf_float(self, pf_total, pf): pf_float = [] for i in range(len(pf)): # 正适应度除以适应度总数，转化为小数 pf_float.append(pf[i] / pf_total) return pf_float # 适应度累加，为分区做准备 def pf_div(self, pf_float): pft_div = [] for j in range(len(pf_float)): # 将适应度转化为轮盘赌的概率 if j == 0: pft_div.append(pf_float[j]) else: pft_div.append(pf_float[j] + pft_div[j-1]) return pft_div # 选择 def selection(self, pcl, pft_div): selected_pop = [] # 挑选后的新种群 select_float = [] # 随机产生的小数 for i in range(len(pcl)): # 产生随机小数 select_float.append(random.random()) for i in range(len(pcl)): # 轮盘赌选择 j = 0 while j < len(pcl): if select_float[i] <= pft_div[j]: # 随机小数与适应度区间对比 selected_pop.append(pcl[j]) break else: j += 1 return selected_pop # 交叉 def crossover(self, selected_pop, population): # 单点交叉 new_pop = [[]] for i in range(len(population)): cpoint = random.randint(0, len(selected_pop[0]) - 1) # 随机生成截取点 # while True: spoint1 = random.randint(0, len(selected_pop) - 1) spoint2 = random.randint(0, len(selected_pop) - 1) if spoint1 == spoint2: continue else: break temp = [] # 染色体片段暂时存储器 temp.extend(selected_pop[spoint1][0:cpoint]) # 第i个染色体0~截取点阶段的基因存储于temp中 temp.extend(selected_pop[spoint2][cpoint:len(selected_pop[0])]) # 第i+1个染色体截取点~末端阶段的基因存储于temp中 new_pop.append(temp) return new_pop[1:] # 变异 def mutation(self, new_pop): for i in range(len(new_pop)): # 对所有染色体进行有概率的变异 mpoint = random.randint(0, len(new_pop[0])-1) # 根据染色体长度，随机生成变异的位数 prandom = random.random() # 随机生成[0,1)之间的小数作为比较概率 if prandom <= self.pm: if new_pop[i][mpoint] == 1: new_pop[i][mpoint] = 0 else: new_pop[i][mpoint] = 1 next_pop = new_pop return next_pop # 二进制转十进制 def b2d(self, value_b): pf_d = [] for i in range(len(value_b)): value_d = 0 for j in range(len(value_b[0])): 

value_d += value_b[i][j] * math.pow(2, len(value_b[0])-1-j) pf_d.append(value_d) return pf_d # 可视化 def plot(self, pcl_d_list, pf, lower_bound, upper_bound, generation, iteration): px = [] for i in range(len(pcl_d_list)): pxt = lower_bound + pcl_d_list[i] * (upper_bound - lower_bound) / (math.pow(2, self.chromosome_length) - 1) px.append(pxt) py = pf sca = plt.scatter(px, py, s=200, lw=0, c='red', alpha=0.5) plt.pause(0.05) print('px', len(px)) j = generation if j < iteration-1: if 'sca' in locals(): sca.remove() # 主程序 def main(self, iteration): population = self.species_origin() # 创建种群 results = [] for i in range(iteration): self.translation(population) all_fitness_list = self.function(population, 0, 2*math.pi) pf = self.positive_fitness(all_fitness_list) pcl = self.positive_chromosome(all_fitness_list, population) self.plot(self.translation(pcl), pf, 0, 2 * np.pi, i, iteration) #print('pcl', pcl) pf_total = self.pf_sum(pf) pf_float = self.pf_float(pf_total, pf) pft_div = self.pf_div(pf_float) #print('pft_div', pft_div) selected_pop = self.selection(pcl, pft_div) new_pop = self.crossover(selected_pop, population) next_pop = self.mutation(new_pop) population = next_pop results.append(pf_total) print('The', i, 'generation pf:', pf, '\npf length', len(pf)) plt.ioff() plt.show() if __name__ == '__main__': ga = GA(100, 17, 0.01) plt.ion() x = np.linspace(0, 2*np.pi, 100) Fx = 2 * np.sin(x) + np.cos(x) plt.plot(x, Fx) plt.grid() ga.main(500) 后记后记 欢迎交流拍砖，但是瞎骂并不可取嗷~ 欢迎交流拍砖，但是瞎骂并不可取嗷 参考文献 参考文献 [1]: https://blog.csdn.net/b2b160/article/details/4680853. [2]: https://blog.csdn.net/acelit/article/details/78187715. [3]: https://blog.csdn.net/quinn1994/article/details/80501542. [4]: https://blog.csdn.net/m0_38101326/article/details/90642193. [5]: https://github.com/MorvanZhou/Evolutionary-Algorithm/blob/master/tutorial- contents/Genetic%20Algorithm/Genetic%20Algorithm%20Basic.py#L72. 作者：闷声小发财