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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/>.
import array
import random
import json
import numpy
from deap import algorithms
from deap import base
from deap import creator
from deap import tools
# gr*.json contains the distance map in list of list style in JSON format
# Optimal solutions are : gr17 = 2085, gr24 = 1272, gr120 = 6942
with open("tsp/gr17.json", "r") as tsp_data:
tsp = json.load(tsp_data)
distance_map = tsp["DistanceMatrix"]
IND_SIZE = tsp["TourSize"]
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", array.array, typecode='i', fitness=creator.FitnessMin)
toolbox = base.Toolbox()
# Attribute generator
toolbox.register("indices", random.sample, range(IND_SIZE), IND_SIZE)
# Structure initializers
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
def evalTSP(individual):
distance = distance_map[individual[-1]][individual[0]]
for gene1, gene2 in zip(individual[0:-1], individual[1:]):
distance += distance_map[gene1][gene2]
return distance,
toolbox.register("mate", tools.cxPartialyMatched)
toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("evaluate", evalTSP)
def main():
random.seed(169)
pop = toolbox.population(n=300)
hof = tools.HallOfFame(1)
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)
algorithms.eaSimple(pop, toolbox, 0.7, 0.2, 40, stats=stats,
halloffame=hof)
return pop, stats, hof
if __name__ == "__main__":
main()
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