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# 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/>.
"""Implementation of the Dynamic Differential Evolution algorithm as presented
in *Mendes and Mohais, 2005, DynDE: A Differential Evolution for Dynamic
Optimization Problems.*
"""
import array
import itertools
import math
import operator
import random
import numpy
from deap import base
from deap.benchmarks import movingpeaks
from deap import creator
from deap import tools
scenario = movingpeaks.SCENARIO_2
NDIM = 5
BOUNDS = [scenario["min_coord"], scenario["max_coord"]]
mpb = movingpeaks.MovingPeaks(dim=NDIM, **scenario)
def brown_ind(iclass, best, sigma):
return iclass(random.gauss(x, sigma) for x in best)
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_float", random.uniform, BOUNDS[0], BOUNDS[1])
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, NDIM)
toolbox.register("brownian_individual", brown_ind, creator.Individual, sigma=0.3)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("select", random.sample, k=4)
toolbox.register("best", tools.selBest, k=1)
toolbox.register("evaluate", mpb)
def main(verbose=True):
NPOP = 10 # Should be equal to the number of peaks
CR = 0.6
F = 0.4
regular, brownian = 4, 2
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "error", "offline_error", "avg", "max"
# Initialize populations
populations = [toolbox.population(n=regular + brownian) for _ in range(NPOP)]
# Evaluate the individuals
for idx, subpop in enumerate(populations):
fitnesses = toolbox.map(toolbox.evaluate, subpop)
for ind, fit in zip(subpop, fitnesses):
ind.fitness.values = fit
record = stats.compile(itertools.chain(*populations))
logbook.record(gen=0, evals=mpb.nevals, error=mpb.currentError(),
offline_error=mpb.offlineError(), **record)
if verbose:
print(logbook.stream)
g = 1
while mpb.nevals < 5e5:
# Detect a change and invalidate fitnesses if necessary
bests = [toolbox.best(subpop)[0] for subpop in populations]
if any(b.fitness.values != toolbox.evaluate(b) for b in bests):
for individual in itertools.chain(*populations):
del individual.fitness.values
# Apply exclusion
rexcl = (BOUNDS[1] - BOUNDS[0]) / (2 * NPOP**(1.0/NDIM))
for i, j in itertools.combinations(range(NPOP), 2):
if bests[i].fitness.valid and bests[j].fitness.valid:
d = sum((bests[i][k] - bests[j][k])**2 for k in range(NDIM))
d = math.sqrt(d)
if d < rexcl:
if bests[i].fitness < bests[j].fitness:
k = i
else:
k = j
populations[k] = toolbox.population(n=regular + brownian)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in itertools.chain(*populations) if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
record = stats.compile(itertools.chain(*populations))
logbook.record(gen=g, evals=mpb.nevals, error=mpb.currentError(),
offline_error=mpb.offlineError(), **record)
if verbose:
print(logbook.stream)
# Evolve the sub-populations
for idx, subpop in enumerate(populations):
newpop = []
xbest, = toolbox.best(subpop)
# Apply regular DE to the first part of the population
for individual in subpop[:regular]:
x1, x2, x3, x4 = toolbox.select(subpop)
offspring = toolbox.clone(individual)
index = random.randrange(NDIM)
for i, value in enumerate(individual):
if i == index or random.random() < CR:
offspring[i] = xbest[i] + F * (x1[i] + x2[i] - x3[i] - x4[i])
offspring.fitness.values = toolbox.evaluate(offspring)
if offspring.fitness >= individual.fitness:
newpop.append(offspring)
else:
newpop.append(individual)
# Apply Brownian to the last part of the population
newpop.extend(toolbox.brownian_individual(xbest) for _ in range(brownian))
# Evaluate the brownian individuals
for individual in newpop[-brownian:]:
individual.fitness.value = toolbox.evaluate(individual)
# Replace the population
populations[idx] = newpop
g += 1
return logbook
if __name__ == "__main__":
main()
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