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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 random
import numpy
from deap import algorithms
from deap import base
from deap import creator
from deap import tools
IND_INIT_SIZE = 5
MAX_ITEM = 50
MAX_WEIGHT = 50
NBR_ITEMS = 20
# To assure reproducibility, the RNG seed is set prior to the items
# dict initialization. It is also seeded in main().
random.seed(64)
# Create the item dictionary: item name is an integer, and value is
# a (weight, value) 2-tuple.
items = {}
# Create random items and store them in the items' dictionary.
for i in range(NBR_ITEMS):
items[i] = (random.randint(1, 10), random.uniform(0, 100))
creator.create("Fitness", base.Fitness, weights=(-1.0, 1.0))
creator.create("Individual", set, fitness=creator.Fitness)
toolbox = base.Toolbox()
# Attribute generator
toolbox.register("attr_item", random.randrange, NBR_ITEMS)
# Structure initializers
toolbox.register("individual", tools.initRepeat, creator.Individual,
toolbox.attr_item, IND_INIT_SIZE)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
def evalKnapsack(individual):
weight = 0.0
value = 0.0
for item in individual:
weight += items[item][0]
value += items[item][1]
if len(individual) > MAX_ITEM or weight > MAX_WEIGHT:
return 10000, 0 # Ensure overweighted bags are dominated
return weight, value
def cxSet(ind1, ind2):
"""Apply a crossover operation on input sets. The first child is the
intersection of the two sets, the second child is the difference of the
two sets.
"""
temp = set(ind1) # Used in order to keep type
ind1 &= ind2 # Intersection (inplace)
ind2 ^= temp # Symmetric Difference (inplace)
return ind1, ind2
def mutSet(individual):
"""Mutation that pops or add an element."""
if random.random() < 0.5:
if len(individual) > 0: # We cannot pop from an empty set
individual.remove(random.choice(sorted(tuple(individual))))
else:
individual.add(random.randrange(NBR_ITEMS))
return individual,
toolbox.register("evaluate", evalKnapsack)
toolbox.register("mate", cxSet)
toolbox.register("mutate", mutSet)
toolbox.register("select", tools.selNSGA2)
def main():
random.seed(64)
NGEN = 50
MU = 50
LAMBDA = 100
CXPB = 0.7
MUTPB = 0.2
pop = toolbox.population(n=MU)
hof = tools.ParetoFront()
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean, axis=0)
stats.register("std", numpy.std, axis=0)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,
halloffame=hof)
return pop, stats, hof
if __name__ == "__main__":
main()
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