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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 25import math
def 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 y
def 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 36import numpy as np
import ObjFunction
class 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 165import numpy as np
from GAIndividual import GAIndividual
import random
import copy
import matplotlib.pyplot as plt
class 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 5if __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