一、实验目的
熟悉和掌握遗传算法的原理、流程和编码策略，并利用遗传求解函数优化问题，理解求解流程并测试主要参数对结果的影响。
二、实验原理
遗传算法的基本思想正是基于模仿生物界遗传学的遗传过程。它把问题的参数用基因代表，把问题的解用染色体代表（在计算机里用二进制码表示），从而得到一个由具有不同染色体的个体组成的群体。这个群体在问题特定的环境里生存竞争，适者有最好的机会生存和个体组成的群体。后代随机化地继承了父代的最好特征，并也在生存环境的控制支配下继续这一过程，群体的染色体都将逐渐适应环境，不断进化，最后收敛到一族最适应环境的类似个体，即得到问题最优的解。
三、实验条件
Python3,Anaconda3,PyCharm
四、实验内容
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import matplotlib.pyplot as pltimport randomimport math#计算函数def f(args): return f2(args)def f1(args): return (3 - (math.sin(2*args[0]))**2 - (math.sin(2*args[1]))**2)def f2(args): x = 1 for i in range(len(args)): z = 0 for j in range(5): z += (j+1) * math.cos(((j+1)+1)*args[i]+(j+1)) x *= z return x#适应函数def s(x): return s2(x)def s1(x): return math.exp(-abs(x-1))def s2(x): return math.exp(-abs(x+187))# 计算2进制序列代表的数值'''解码并计算值group 染色体chrom_length 染色体长度max_value, min_value 上下限div 分界点'''def b2d(b, chrom_length, max_value, min_value, div): rwno = [] #因为染色体里面有多个变量，所以需要div来分割 for i in range(len(div)): if i == 0: star = 0 end = div[i] else: star = div[i-1] + 1 end = div[i] t = 0 for j in range(star, end): # 分隔参数[1,2,3||4,5,6] t += b[j] * (math.pow(2, j - star)) t = t * max_value / (math.pow(2, end - star + 1) - 1) - min_value rwno.append(t) return rwno # 这是一个list'''计算当前函数值group 染色体chrom_length 染色体长度max_value，min_value 最大最小值divid 分割'''def calobjValue(group, chrom_length, max_value, min_value, divid): obj_value = [] for i in range(len(group)): x = b2d(group[i], chrom_length, max_value, min_value, divid)#这里面可能是多个变量 obj_value.append(f(x)) return obj_value# 获取适应值def calfitValue(obj_value): fit_value = [] for i in range(len(obj_value)): temp = s(obj_value[i]) # 调用适应函数计算 fit_value.append(temp) return fit_value#累计适应值方便计算平均def sum_fit(fit_value): total = 0 for i in range(len(fit_value)): total += fit_value[i] return total# 转轮盘选择法def selection(group, fit_value): newfit_value = [] #[ [[染色体], [锚点]],... ] newgroup = [] #[ [父], [母], [父], [母],....] # 适应度总和 total_fit = sum_fit(fit_value) # 设置各个的锚点 t = 0 for i in range(len(group)): t += fit_value[i]/total_fit newfit_value.append([group[i], t]) # 转轮盘选择法 for i in range(len(newfit_value)): parents = len(newfit_value) # 初始化指针 r = random.random() #指针 for j in range(len(newfit_value)):#看看指针指到睡了 if newfit_value[j][1] > r: parents = j break newgroup.append(newfit_value[parents][0]) return newgroup# 交配def crossover(group, fit_value, pc): parents_group = selection(group, fit_value) #[ [[父], [母]],....] group_len = len(parents_group) for i in range(0, group_len, 2): if(random.random() < pc): # 看看是否要交配 cpoint = random.randint(0, len(parents_group[0])) # 随机交叉点 temp1 = [] temp2 = [] temp1.extend(parents_group[i][0:cpoint]) temp1.extend(parents_group[i+1][cpoint:len(parents_group[i])]) temp2.extend(parents_group[i+1][0:cpoint]) temp2.extend(parents_group[i][cpoint:len(parents_group[i])]) group[i] = temp1 group[i+1] = temp2# 基因突变def mutation(group, pm): px = len(group) py = len(group[0]) for i in range(px): # 遍历 if(random.random() < pm): mpoint = random.randint(0, py-1) # 取要变异哪个 if(group[i][mpoint] == 1): group[i][mpoint] = 0 else: group[i][mpoint] = 1'''找出最优解和最优解的基因编码group 种群染色去fit_value 种群适应'''def best(group, fit_value): px = len(group) best_in = group[0] best_fit = fit_value[0] for i in range(1, px): if(fit_value[i] > best_fit): best_fit = fit_value[i] best_in = group[i] #print(best_in) return [best_in, best_fit]'''创建初代种群group_size 种群大小chrom_length 染色体长度'''def getFisrtGroup(group_size, chrom_length): #print('初代种群：') group = [] for i in range(group_size): temp = [] for j in range(chrom_length): temp.append(random.randint(0, 1)) group.append(temp) #print(group) return groupgeneration = 50 # 繁衍代数（数量越小，出结果脍，迭代次数越少）group_size = 400 # 染色体数量，偶数max_value = 20 # 范围min_value = 10 # 偏移修正chrom_length = 800 # 染色体长度divid = [399, chrom_length-1] # 输入值分界点， 最后一位必须是染色体长度pc = 0.7 # 交配概率pm = 0.1 # 变异概率results = [] # 存储每一代的最优解fit_value = [] # 个体适应度points = [] #多个最优解#生成初代group = getFisrtGroup(group_size, chrom_length)for i in range(generation): if i > 100: pm = 0.01 if i > 1000: pm = 0.001 obj_value = calobjValue(group, chrom_length, max_value, min_value, divid) # 个体评价 fit_value = calfitValue(obj_value) # 获取群体适应值 best_individual, best_fit = best(group, fit_value) # 返回最优基因, 最优适应值 xx = b2d(best_individual, chrom_length, max_value, min_value, divid) if( abs(f(xx)+186.730909) < 0.000001):#找到最优解 flag = False for p in points: if( (abs(xx[0]-p[0]) < 0.1) and (abs(xx[1]-p[1]) < 0.1) ):#剔除重复解 flag = True break if flag == False: print(xx) points.append(xx) results.append([i, best_fit, b2d(best_individual, chrom_length, max_value, min_value, divid), best_individual]) #加进坐标里 crossover(group, fit_value, pc) # 交配 mutation(group, pm) # 变异#results.sort(key=lambda x:x[1])rank = sorted(results, key=lambda x:x[1])#print('\n', rank[-1])#print(results)x = b2d(rank[-1][3], chrom_length, max_value, min_value, divid)#最终结果print("f(x) = " , f(x) , "x = " , x , " 染色体 = ", rank[-1][3], " 适应值 = ", rank[-1][1], "代数：", rank[-1][0])#输出适应图X = []Y = []for i in range(generation): X.append(i) Y.append(results[i][1])plt.plot(X, Y)plt.show() |
五、实验结果
以上就是python人工智能遗传算法示例解析的详细内容，更多关于python人工智能遗传算法的资料请关注服务器之家其它相关文章！
原文链接：https://blog.csdn.net/weixin_44382897/article/details/106683626
