def crossover(pop, pc, fit_value, chrom_length, max_value):
"""
:param pop: 二进制种群
:param pc: 交配概率
:param fit_value:
:param chrom_length: 编码基因长度
:param max_value: 最大值限制
:return: 交叉后新种群，新种群适应度
"""
pop_len = len(pop)
for i in range(pop_len):
if random.random() < pc:
mother, father = findMoFa(pop, fit_value, chrom_length, max_value)
cpoint = random.randint(1, len(pop[0]) - 2) # 随机交叉点（单点交叉策略）
temp1 = []
temp2 = []
temp1.extend(mother[0:cpoint])
temp1.extend(father[cpoint:chrom_length])
temp2.extend(father[0:cpoint])
temp2.extend(mother[cpoint:chrom_length])
pop.append(temp1)
pop.append(temp2)
fit_value = calobjValue(pop, chrom_length, max_value)
return pop, fit_value
def crossover(pop, pc, fit_value, chrom_length, max_value):
"""
:param pop: 二进制种群
:param pc: 交配概率
:param fit_value:
:param chrom_length: 编码基因长度
:param max_value: 最大值限制
:return: 交叉后新种群，新种群适应度
"""
pop_len = len(pop)
for i in range(pop_len):
if random.random() < pc:
# mother, father = findMoFa(pop, fit_value, chrom_length, max_value)
mother, mother_fit = best(pop, fit_value)
father = pop[i]
cpoint = random.randint(0, chrom_length - 1) # 随机交叉点（单点交叉策略）
new_one1 = []
new_one2 = []
for j in range(len(mother)):
temp1 = []
temp2 = []
temp1.extend(mother[j][0:cpoint])
temp1.extend(father[j][cpoint:chrom_length])
temp2.extend(father[j][0:cpoint])
temp2.extend(mother[j][cpoint:chrom_length])
new_one1.append(temp1)
new_one2.append(temp2)
pop.append(new_one1)
pop.append(new_one2)
pop = calfitValue(pop, chrom_length, max_value)
fit_value = calobjValue(pop, chrom_length, max_value)
return pop, fit_value
def selection(pop, fit_value, pop_size, chrom_length, max_value):
"""
:param pop:
:param fit_value:
:return:
"""
n = len(pop)
max_p, max_fit = best(pop, fit_value)
newpop = [] # 选择后新种群
newpop.append(max_p)
# print(len(newpop))
# 概率累加计算
newfit_value = cumsum(fit_value)
# 轮盘选择
# n = pop_size - 1
# while n > 0:
# choose = random.random()
# for j in range(n):
# if newfit_value[j] >= choose:
# if boolcondition(pop[j]):
# n -= 1
# newpop.append(pop[j])
# break
for i in range(pop_size - 1):
choose = random.random()
for j in range(n):
if newfit_value[j] >= choose:
newpop.append(pop[j])
break
return newpop, calobjValue(newpop, chrom_length, max_value)
def mutation(pop, pm, chrom_length, max_value):
"""
基因突变
:param pop:二进制种群
:param pm: 变异概率
:return: 变异后新种群，适应度
"""
px = len(pop)
py = len(pop[0])
for i in range(px):
if random.random() < pm:
m = random.randint(1, py) # 随机变异m个点
for j in range(m):
point = random.randint(0, py - 1) # 随机生成变异点位置
if pop[i][point] == 1:
pop[i][point] = 0
else:
pop[i][point] = 1
return pop, calobjValue(pop, chrom_length, max_value)
def mutation(pop, pm, chrom_length, max_value):
"""
基因突变
:param pop:二进制种群
:param pm: 变异概率
:return: 变异后新种群，适应度
"""
pop_size = len(pop)
type_num = len(pop[0])
for i in range(pop_size):
if random.random() < pm:
# x = random.randint(0, type_num) # 随机变异的基因组下标
x = i # 随机变异的基因组下标
for j in range(type_num):
m = random.randint(1, chrom_length) # 变异点个数
for z in range(m):
point = random.randint(0, chrom_length - 1) # 随机生成变异点位置
if pop[x][j][point] == 1:
pop[x][j][point] = 0
else:
pop[x][j][point] = 1
pop = calfitValue(pop, chrom_length, max_value)
return pop, calobjValue(pop, chrom_length, max_value)
pop_size = 10 # 种群数量
iterations = 500 # 迭代进化次数
max_value = 10 # 基因中允许出现的最大值
chrom_length = 10 # 染色体长度
pc = 0.6 # 交配概率
pm = 0.01 # 变异概率
results = [] # 每代最优解集
fit_value = [] # 个体适应度
fit_mean = [] # 平均适应度
pop = geneEncoding(pop_size, chrom_length) # 初始化二进制种群基因编码
for i in range(iterations):
# print('NO. ', i + 1)
fit_value = calobjValue(pop, chrom_length, max_value) # 适应度计算
# print('初始', fit_value)
# fit_value = calfitValue(obj_value) # 筛选淘汰
best_indiviual, best_fit = best(pop, fit_value) # 最优解集
best_indiviual_ten = decoding(best_indiviual, chrom_length,
max_value) # 最优解集进制转换
results.append([best_indiviual_ten, best_fit])
pop, fit_value = selection(pop, fit_value, pop_size, chrom_length,
max_value)
# print('选择', fit_value)
pop, fit_value = crossover(pop, pc, fit_value, chrom_length, max_value)
# print('交叉', fit_value)
pop, fit_value = mutation(pop, pm, chrom_length, max_value)
# print('变异', fit_value)
print(results[iterations - 1][1])