1#    This file is part of EAP.
2#
3#    EAP is free software: you can redistribute it and/or modify
4#    it under the terms of the GNU Lesser General Public License as
5#    published by the Free Software Foundation, either version 3 of
6#    the License, or (at your option) any later version.
7#
8#    EAP is distributed in the hope that it will be useful,
9#    but WITHOUT ANY WARRANTY; without even the implied warranty of
10#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
11#    GNU Lesser General Public License for more details.
12#
13#    You should have received a copy of the GNU Lesser General Public
14#    License along with EAP. If not, see <http://www.gnu.org/licenses/>.
15
16
17import random
18import array
19
20import numpy
21
22from itertools import chain
23
24from deap import base
25from deap import benchmarks
26from deap import creator
27from deap import tools
28
29# Problem dimension
30NDIM = 10
31
32creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
33creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin)
34
35def mutDE(y, a, b, c, f):
36    size = len(y)
37    for i in range(len(y)):
38        y[i] = a[i] + f*(b[i]-c[i])
39    return y
40
41def cxBinomial(x, y, cr):
42    size = len(x)
43    index = random.randrange(size)
44    for i in range(size):
45        if i == index or random.random() < cr:
46            x[i] = y[i]
47    return x
48
49def cxExponential(x, y, cr):
50    size = len(x)
51    index = random.randrange(size)
52    # Loop on the indices index -> end, then on 0 -> index
53    for i in chain(range(index, size), range(0, index)):
54        x[i] = y[i]
55        if random.random() < cr:
56            break
57    return x
58
59toolbox = base.Toolbox()
60toolbox.register("attr_float", random.uniform, -3, 3)
61toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, NDIM)
62toolbox.register("population", tools.initRepeat, list, toolbox.individual)
63toolbox.register("mutate", mutDE, f=0.8)
64toolbox.register("mate", cxExponential, cr=0.8)
65toolbox.register("select", tools.selRandom, k=3)
66toolbox.register("evaluate", benchmarks.griewank)
67
68def main():
69    # Differential evolution parameters
70    MU = NDIM * 10
71    NGEN = 200
72
73    pop = toolbox.population(n=MU);
74    hof = tools.HallOfFame(1)
75    stats = tools.Statistics(lambda ind: ind.fitness.values)
76    stats.register("avg", numpy.mean)
77    stats.register("std", numpy.std)
78    stats.register("min", numpy.min)
79    stats.register("max", numpy.max)
80
81    logbook = tools.Logbook()
82    logbook.header = "gen", "evals", "std", "min", "avg", "max"
83
84    # Evaluate the individuals
85    fitnesses = toolbox.map(toolbox.evaluate, pop)
86    for ind, fit in zip(pop, fitnesses):
87        ind.fitness.values = fit
88
89    record = stats.compile(pop)
90    logbook.record(gen=0, evals=len(pop), **record)
91    print(logbook.stream)
92
93    for g in range(1, NGEN):
94        children = []
95        for agent in pop:
96            # We must clone everything to ensure independance
97            a, b, c = [toolbox.clone(ind) for ind in toolbox.select(pop)]
98            x = toolbox.clone(agent)
99            y = toolbox.clone(agent)
100            y = toolbox.mutate(y, a, b, c)
101            z = toolbox.mate(x, y)
102            del z.fitness.values
103            children.append(z)
104
105        fitnesses = toolbox.map(toolbox.evaluate, children)
106        for (i, ind), fit in zip(enumerate(children), fitnesses):
107            ind.fitness.values = fit
108            if ind.fitness > pop[i].fitness:
109                pop[i] = ind
110
111        hof.update(pop)
112        record = stats.compile(pop)
113        logbook.record(gen=g, evals=len(pop), **record)
114        print(logbook.stream)
115
116    print("Best individual is ", hof[0])
117    print("with fitness", hof[0].fitness.values[0])
118    return logbook
119
120if __name__ == "__main__":
121    main()
122