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NSGA-II-DEAP-SolQ2.py
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NSGA-II-DEAP-SolQ2.py
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# This file is part of DEAP.
# This implements the NSGA-II in an easy way because it makes us of DEAP subroutines
# The non dominated sort and crowding distance are solved by a simiple call to DEAP subroutines
# and their implementation is hidden.
#
# 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 math import sqrt
from deap import algorithms
from deap import base
from deap import benchmarks
from deap.benchmarks.tools import diversity, convergence, hypervolume
from deap import creator
from deap import tools
import matplotlib.pyplot as plt
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
creator.create("Individual", list, fitness=creator.FitnessMin)
toolbox = base.Toolbox()
def calcFitness(individual):
# x1 in [1, 2,x, 16]; x2 in [1, 2,x, 8]; x3 in [1, 2, 3, 4]
# 4 bits 3 bits 2 bits
x1=individual[0:4]
x2=individual[4:7]
x3=individual[7:9]
x1= int("".join(str(i) for i in x1),2)
x2= int("".join(str(i) for i in x2),2)
x3= int("".join(str(i) for i in x3),2)
f1=((x1/4.0)**2+(x2/2.0)**2+x3**2.0)/3.0
f2=((x1/4.0-2.0)**2+(x2/2.0-2.0)**2+(x3-2.0)**2.0)/3.0
return f1,f2
toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual,
toolbox.attr_bool, 9)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", calcFitness)
toolbox.register("mate", tools.cxTwoPoint)
flipProb=1.0/9
toolbox.register("mutate", tools.mutFlipBit, indpb=flipProb)
toolbox.register("select", tools.selNSGA2)
def main(seed=None):
random.seed(seed)
NGEN = 250
MU = 100
CXPB = 0.9
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)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
pop = toolbox.population(n=MU)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
# selTournamentDCD means Tournament selection based on dominance (D)
# followed by crowding distance (CD). This selection requires the
# individuals to have a crowding_dist attribute
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
#make pairs of all (even,odd) in offspring
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.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
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
print("Final population hypervolume is %f" % hypervolume(pop, [11.0, 11.0]))
return pop, logbook
if __name__ == "__main__":
pop, stats = main()
pop.sort(key=lambda x: x.fitness.values)
front = numpy.array([ind.fitness.values for ind in pop])
plt.scatter(front[:,0], front[:,1], c="b")
plt.axis("tight")
plt.show()
# print some individuals
for n in range(10):
i=pop[random.choice(range(0, len(pop)))]
x1=i[0:4]
x2=i[4:7]
x3=i[7:9]
x1= int("".join(str(i) for i in x1),2)
x2= int("".join(str(i) for i in x2),2)
x3= int("".join(str(i) for i in x3),2)
print(x1,x2,x3)