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
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
| ## {{{ http://code.activestate.com/recipes/199121/ (r1)
#
# genetic.py
#
import random
MAXIMIZE, MINIMIZE = 11, 22
class Individual(object):
alleles = (0,1)
length = 30
seperator = ''
optimization = MINIMIZE
def __init__(self, chromosome=None):
self.chromosome = chromosome or self._makechromosome()
self.score = None # set during evaluation
def _makechromosome(self):
"makes a chromosome from randomly selected alleles."
return [random.choice(self.alleles) for gene in range(self.length)]
def evaluate(self, optimum=None):
"this method MUST be overridden to evaluate individual fitness score."
pass
def crossover(self, other):
"override this method to use your preferred crossover method."
return self._twopoint(other)
def mutate(self, gene):
"override this method to use your preferred mutation method."
self._pick(gene)
# sample mutation method
def _pick(self, gene):
"chooses a random allele to replace this gene's allele."
self.chromosome[gene] = random.choice(self.alleles)
# sample crossover method
def _twopoint(self, other):
"creates offspring via two-point crossover between mates."
left, right = self._pickpivots()
def mate(p0, p1):
chromosome = p0.chromosome[:]
chromosome[left:right] = p1.chromosome[left:right]
child = p0.__class__(chromosome)
child._repair(p0, p1)
return child
return mate(self, other), mate(other, self)
# some crossover helpers ...
def _repair(self, parent1, parent2):
"override this method, if necessary, to fix duplicated genes."
pass
def _pickpivots(self):
left = random.randrange(1, self.length-2)
right = random.randrange(left, self.length-1)
return left, right
#
# other methods
#
def __repr__(self):
"returns string representation of self"
return '<%s chromosome="%s" score=%s>' % \
(self.__class__.__name__,
self.seperator.join(map(str,self.chromosome)), self.score)
def __cmp__(self, other):
if self.optimization == MINIMIZE:
return cmp(self.score, other.score)
else: # MAXIMIZE
return cmp(other.score, self.score)
def copy(self):
twin = self.__class__(self.chromosome[:])
twin.score = self.score
return twin
class Environment(object):
def __init__(self, kind, population=None, size=100, maxgenerations=100,
crossover_rate=0.90, mutation_rate=0.01, optimum=None):
self.kind = kind
self.size = size
self.optimum = optimum
self.population = population or self._makepopulation()
for individual in self.population:
individual.evaluate(self.optimum)
self.crossover_rate = crossover_rate
self.mutation_rate = mutation_rate
self.maxgenerations = maxgenerations
self.generation = 0
self.report()
def _makepopulation(self):
return [self.kind() for individual in range(self.size)]
def run(self):
while not self._goal():
self.step()
def _goal(self):
return self.generation > self.maxgenerations or \
self.best.score == self.optimum
def step(self):
self.population.sort()
self._crossover()
self.generation += 1
self.report()
def _crossover(self):
next_population = [self.best.copy()]
while len(next_population) < self.size:
mate1 = self._select()
if random.random() < self.crossover_rate:
mate2 = self._select()
offspring = mate1.crossover(mate2)
else:
offspring = [mate1.copy()]
for individual in offspring:
self._mutate(individual)
individual.evaluate(self.optimum)
next_population.append(individual)
self.population = next_population[:self.size]
def _select(self):
"override this to use your preferred selection method"
return self._tournament()
def _mutate(self, individual):
for gene in range(individual.length):
if random.random() < self.mutation_rate:
individual.mutate(gene)
#
# sample selection method
#
def _tournament(self, size=8, choosebest=0.90):
competitors = [random.choice(self.population) for i in range(size)]
competitors.sort()
if random.random() < choosebest:
return competitors[0]
else:
return random.choice(competitors[1:])
def best():
doc = "individual with best fitness score in population."
def fget(self):
return self.population[0]
return locals()
best = property(**best())
def report(self):
print "="*70
print "generation: ", self.generation
print "best: ", self.best
---------------------------------------------------------------------
#
# onemax.py - useage example
#
# the fittest individual will have a chromosome consisting of 30 '1's
#
import genetic
class OneMax(genetic.Individual):
optimization = genetic.MAXIMIZE
def evaluate(self, optimum=None):
self.score = sum(self.chromosome)
def mutate(self, gene):
self.chromosome[gene] = not self.chromosome[gene] # bit flip
if __name__ == "__main__":
env = genetic.Environment(OneMax, maxgenerations=1000, optimum=30)
env.run()
## end of http://code.activestate.com/recipes/199121/ }}} |
Algorithme génétique ou colonnie de fourmies (développement d'un IRS)
Répondre avec citation
Envoyé par MSN9149
Partager