1 import
numpy
2import
matplotlib.pyplot as plt
3import
random
4import
operator
56 x_label=
7 y_label=#
將每一步迭代的結果儲存到列表中,便於畫圖
8class
ga(object):
9def
__init__
(self,length,number,lower_boundary,upper_boundary,iter_number):
10 self.length = length#
確定染色體編碼長度
11 self.number = number#
確定初始化種群數量
12 self.lower_boundary = lower_boundary#
定義域下界
13 self.upper_boundary = upper_boundary#
定義域上屆
14 self.population =self.initial_population(length,number)
15self.iteration(iter_number)
16def initial_population(self,length,number):#
初始化種群
17return [self.initial_chromosome(length) for i in
range(number)]
18def initial_chromosome(self,length):#
編碼19
"""20
隨機生成長度為length的染色體，每個基因的取值是0或1
21這裡用乙個bit表示乙個基因
22"""
23 chromosome =0
24for i in
range(self.length):
25 chromosome |= (1 << i) * random.randint(0, 1)26#
print(chromosome)27#
print(num)
28return
chromosome
29def decode(self,chromosome):#
解碼30 x = chromosome*(self.upper_boundary-self.lower_boundary)/(2**self.length-1)
31returnx32
def fitness_function(self,chromosome):#
適應度函式
33 x =self.decode(chromosome)
34 y = x + 10 * numpy.sin(5 * x) + 7 * numpy.cos(4 *x)
35returny36
def evolution(self, retain_rate=0.2, random_select_rate=0.5, mutation_rate=0.01):
37"""
38對當前種群依次進行選擇、交叉並生成新一代種群，然後對新一代種群進行變異
39"""
40 parents =self.selection(retain_rate, random_select_rate)
41self.crossover(parents)
42self.mutation(mutation_rate)
43def
selection(self,retain_rate, random_select_rate):
44 graded = [(self.fitness_function(chromosome), chromosome) for chromosome in
self.population]
45 sort = [x[1] for x in sorted(graded, reverse=true)]
46 retain_length=int(len(sort)*retain_rate)47#
選出適應性強的個體，精英選擇
48 parents=sort[:retain_length]
49for chromosome in
sort[retain_length:]:
50if random.random()
5152
return
parents
53def crossover(self,parents):#
交叉54 children=55#
需要繁殖的子代數量
56 target_number=len(self.population)-len(parents)57#
開始繁殖
58while len(children)
59 father = random.randint(0, len(parents) - 1)
60 mother = random.randint(0, len(parents) - 1)
61if father !=mother:62#
隨機選取交叉點
63 cross_point =random.randint(0, self.length)64#
生成掩碼，方便位操作
65 mark =0
66for i in
range(cross_point):
67 mark |= (1 <
68 father =parents[father]
69 mother =parents[mother]70#
孩子將獲得父親在交叉點前的基因和母親在交叉點後（包括交叉點）的基因
71 child = ((father & mark) | (mother & ~mark)) & ((1 << self.length) - 1)
7273
#經過繁殖後，孩子和父母的數量與原始種群數量相等，在這裡可以更新種群。
74 self.population = parents +children
75def
mutation(self,rate):
76for i in
range(len(self.population)):
77if random.random()
78 j = random.randint(0, self.length - 1)#
s隨機產生變異位置
79 self.population[i] ^= 1 << j #
產生變異
80def
result(self):
81'''
82獲得近優值
83'''
84 graded = [(self.fitness_function(chromosome), chromosome) for chromosome in
self.population]
85 graded = [x[1] for x in sorted(graded, reverse=true)]86#
print('近優的x值為：', self.decode(graded[0]),87#
'近優的y值為：', self.fitness_function(graded[0]))88#
將每一步迭代的結果儲存到列表中
8990
return
self.decode(graded[0])
91def
iteration(self,iter_number):
92for i in
range(iter_number):
93self.evolution()
94self.result()
9596 g1=ga(17,300,0,9,30)#
建立乙個例項
簡單遺傳演算法求函式極值
引言 遺傳演算法求函式極值算是遺傳演算法的一種最簡單的應用，這裡就介紹一種簡單的，全文基本翻譯自codeproject的一篇文章，作者為luay al wesi，軟體工程師。例子中的函式為y x2 5 大家可以將其改為其他複雜一些的函式，比如說f x 10sin 5x 7cos 4x 等。本篇文章適...
python 遺傳演算法求函式極值問題
遺傳演算法實現求函式極大值 zjh import numpy as np import random import matplotlib.pyplot as plt class ga 求出二進位制編碼的長度 def init self self.boundsbegin 2 self.boundsen...
遺傳演算法求函式最優解
前兩天寫了乙個遺傳演算法求最優解的程式，今天拿出來簡單整理一下。最優解問題 四個變數取何值，該函式取最大值時？取值範圍 5到5 顯然，當四個未知數都為零的時候，函式取最大值，但如何利用遺傳演算法來實現，才是要研究的問題。1.染色體結構定義define.h struct chromosome 基因 d...