ObjFunction.py
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
|
import mathdef GrieFunc(vardim, x, bound): """ Griewangk function """ s1 = 0. s2 = 1. for i in range(1, vardim + 1): s1 = s1 + x[i - 1] ** 2 s2 = s2 * math.cos(x[i - 1] / math.sqrt(i)) y = (1. / 4000.) * s1 - s2 + 1 y = 1. / (1. + y) return ydef RastFunc(vardim, x, bound): """ Rastrigin function """ s = 10 * 25 for i in range(1, vardim + 1): s = s + x[i - 1] ** 2 - 10 * math.cos(2 * math.pi * x[i - 1]) return s |
GAIndividual.py
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
|
import numpy as npimport ObjFunctionclass GAIndividual: ''' individual of genetic algorithm ''' def __init__(self, vardim, bound): ''' vardim: dimension of variables bound: boundaries of variables ''' self.vardim = vardim self.bound = bound self.fitness = 0. def generate(self): ''' generate a random chromsome for genetic algorithm ''' len = self.vardim rnd = np.random.random(size=len) self.chrom = np.zeros(len) for i in xrange(0, len): self.chrom[i] = self.bound[0, i] + \ (self.bound[1, i] - self.bound[0, i]) * rnd[i] def calculateFitness(self): ''' calculate the fitness of the chromsome ''' self.fitness = ObjFunction.GrieFunc( self.vardim, self.chrom, self.bound) |
GeneticAlgorithm.py
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
|
import numpy as npfrom GAIndividual import GAIndividualimport randomimport copyimport matplotlib.pyplot as pltclass GeneticAlgorithm: ''' The class for genetic algorithm ''' def __init__(self, sizepop, vardim, bound, MAXGEN, params): ''' sizepop: population sizepop vardim: dimension of variables bound: boundaries of variables MAXGEN: termination condition param: algorithm required parameters, it is a list which is consisting of crossover rate, mutation rate, alpha ''' self.sizepop = sizepop self.MAXGEN = MAXGEN self.vardim = vardim self.bound = bound self.population = [] self.fitness = np.zeros((self.sizepop, 1)) self.trace = np.zeros((self.MAXGEN, 2)) self.params = params def initialize(self): ''' initialize the population ''' for i in xrange(0, self.sizepop): ind = GAIndividual(self.vardim, self.bound) ind.generate() self.population.append(ind) def evaluate(self): ''' evaluation of the population fitnesses ''' for i in xrange(0, self.sizepop): self.population[i].calculateFitness() self.fitness[i] = self.population[i].fitness def solve(self): ''' evolution process of genetic algorithm ''' self.t = 0 self.initialize() self.evaluate() best = np.max(self.fitness) bestIndex = np.argmax(self.fitness) self.best = copy.deepcopy(self.population[bestIndex]) self.avefitness = np.mean(self.fitness) self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness print("Generation %d: optimal function value is: %f; average function value is %f" % ( self.t, self.trace[self.t, 0], self.trace[self.t, 1])) while (self.t < self.MAXGEN - 1): self.t += 1 self.selectionOperation() self.crossoverOperation() self.mutationOperation() self.evaluate() best = np.max(self.fitness) bestIndex = np.argmax(self.fitness) if best > self.best.fitness: self.best = copy.deepcopy(self.population[bestIndex]) self.avefitness = np.mean(self.fitness) self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness print("Generation %d: optimal function value is: %f; average function value is %f" % ( self.t, self.trace[self.t, 0], self.trace[self.t, 1])) print("Optimal function value is: %f; " % self.trace[self.t, 0]) print "Optimal solution is:" print self.best.chrom self.printResult() def selectionOperation(self): ''' selection operation for Genetic Algorithm ''' newpop = [] totalFitness = np.sum(self.fitness) accuFitness = np.zeros((self.sizepop, 1)) sum1 = 0. for i in xrange(0, self.sizepop): accuFitness[i] = sum1 + self.fitness[i] / totalFitness sum1 = accuFitness[i] for i in xrange(0, self.sizepop): r = random.random() idx = 0 for j in xrange(0, self.sizepop - 1): if j == 0 and r < accuFitness[j]: idx = 0 break elif r >= accuFitness[j] and r < accuFitness[j + 1]: idx = j + 1 break newpop.append(self.population[idx]) self.population = newpop def crossoverOperation(self): ''' crossover operation for genetic algorithm ''' newpop = [] for i in xrange(0, self.sizepop, 2): idx1 = random.randint(0, self.sizepop - 1) idx2 = random.randint(0, self.sizepop - 1) while idx2 == idx1: idx2 = random.randint(0, self.sizepop - 1) newpop.append(copy.deepcopy(self.population[idx1])) newpop.append(copy.deepcopy(self.population[idx2])) r = random.random() if r < self.params[0]: crossPos = random.randint(1, self.vardim - 1) for j in xrange(crossPos, self.vardim): newpop[i].chrom[j] = newpop[i].chrom[ j] * self.params[2] + (1 - self.params[2]) * newpop[i + 1].chrom[j] newpop[i + 1].chrom[j] = newpop[i + 1].chrom[j] * self.params[2] + \ (1 - self.params[2]) * newpop[i].chrom[j] self.population = newpop def mutationOperation(self): ''' mutation operation for genetic algorithm ''' newpop = [] for i in xrange(0, self.sizepop): newpop.append(copy.deepcopy(self.population[i])) r = random.random() if r < self.params[1]: mutatePos = random.randint(0, self.vardim - 1) theta = random.random() if theta > 0.5: newpop[i].chrom[mutatePos] = newpop[i].chrom[ mutatePos] - (newpop[i].chrom[mutatePos] - self.bound[0, mutatePos]) * (1 - random.random() ** (1 - self.t / self.MAXGEN)) else: newpop[i].chrom[mutatePos] = newpop[i].chrom[ mutatePos] + (self.bound[1, mutatePos] - newpop[i].chrom[mutatePos]) * (1 - random.random() ** (1 - self.t / self.MAXGEN)) self.population = newpop def printResult(self): ''' plot the result of the genetic algorithm ''' x = np.arange(0, self.MAXGEN) y1 = self.trace[:, 0] y2 = self.trace[:, 1] plt.plot(x, y1, 'r', label='optimal value') plt.plot(x, y2, 'g', label='average value') plt.xlabel("Iteration") plt.ylabel("function value") plt.title("Genetic algorithm for function optimization") plt.legend() plt.show() |
运行程序：
|
1
2
3
4
5
|
if __name__ == "__main__": bound = np.tile([[-600], [600]], 25) ga = GA(60, 25, bound, 1000, [0.9, 0.1, 0.5]) ga.solve() |
作者：Alex Yu
出处：http://www.cnblogs.com/biaoyu/
以上就是python实现简单遗传算法的详细内容，更多关于python 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://www.cnblogs.com/biaoyu/p/4857881.html
