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
|
# This file is part of DEAP.
#
# DEAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# DEAP is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
import random
from deap import algorithms
from deap import base
from deap import creator
from deap import tools
from deap import cTools
import sortingnetwork as sn
INPUTS = 6
def evalEvoSN(individual, dimension):
network = sn.SortingNetwork(dimension, individual)
return network.assess(), network.length, network.depth
def genWire(dimension):
return (random.randrange(dimension), random.randrange(dimension))
def genNetwork(dimension, min_size, max_size):
size = random.randint(min_size, max_size)
return [genWire(dimension) for i in range(size)]
def mutWire(individual, dimension, indpb):
for index, elem in enumerate(individual):
if random.random() < indpb:
individual[index] = genWire(dimension)
def mutAddWire(individual, dimension):
index = random.randint(0, len(individual))
individual.insert(index, genWire(dimension))
def mutDelWire(individual):
index = random.randrange(len(individual))
del individual[index]
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0, -1.0))
creator.create("Individual", list, fitness=creator.FitnessMin)
toolbox = base.Toolbox()
# Gene initializer
toolbox.register("network", genNetwork, dimension=INPUTS, min_size=9, max_size=12)
# Structure initializers
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.network)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", evalEvoSN, dimension=INPUTS)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", mutWire, dimension=INPUTS, indpb=0.05)
toolbox.register("addwire", mutAddWire, dimension=INPUTS)
toolbox.register("delwire", mutDelWire)
toolbox.register("select", cTools.selNSGA2)
def main():
random.seed(64)
population = toolbox.population(n=300)
hof = tools.ParetoFront()
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("Avg", tools.mean)
stats.register("Std", tools.std)
stats.register("Min", min)
stats.register("Max", max)
CXPB, MUTPB, ADDPB, DELPB, NGEN = 0.5, 0.2, 0.01, 0.01, 40
# Evaluate every individuals
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
hof.update(population)
stats.update(population)
# Begin the evolution
for g in range(NGEN):
print("-- Generation %i --" % g)
offspring = [toolbox.clone(ind) for ind in population]
# Apply crossover and mutation
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() < CXPB:
toolbox.mate(ind1, ind2)
del ind1.fitness.values
del ind2.fitness.values
# Note here that we have a different scheme of mutation than in the
# original algorithm, we use 3 different mutations subsequently.
for ind in offspring:
if random.random() < MUTPB:
toolbox.mutate(ind)
del ind.fitness.values
if random.random() < ADDPB:
toolbox.addwire(ind)
del ind.fitness.values
if random.random() < DELPB:
toolbox.delwire(ind)
del ind.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
print(" Evaluated %i individuals" % len(invalid_ind))
population = toolbox.select(population+offspring, len(offspring))
hof.update(population)
stats.update(population)
print(" Min %s" % stats.Min[0][-1][0])
print(" Max %s" % stats.Max[0][-1][0])
print(" Avg %s" % stats.Avg[0][-1][0])
print(" Std %s" % stats.Std[0][-1][0])
best_network = sn.SortingNetwork(INPUTS, hof[0])
print(best_network)
print(best_network.draw())
print("%i errors, length %i, depth %i" % hof[0].fitness.values)
return population, stats, hof
if __name__ == "__main__":
main()
|
