Python StandardGeneticAlgorithm Exemples

Exemple #1

Fichier : fourpeaks.py Projet : nharper32/ABAGAIL

ef = FourPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)

rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200000)
fit.train()
print "RHC: " + str(ef.value(rhc.getOptimal()))

sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, 200000)
fit.train()
print "SA: " + str(ef.value(sa.getOptimal()))

ga = StandardGeneticAlgorithm(200, 100, 10, gap)
fit = FixedIterationTrainer(ga, 1000)
fit.train()
print "GA: " + str(ef.value(ga.getOptimal()))

mimic = MIMIC(200, 20, pop)
fit = FixedIterationTrainer(mimic, 1000)
fit.train()
print "MIMIC: " + str(ef.value(mimic.getOptimal()))

Exemple #2

Fichier : fourpeaks.py Projet : onaclovtech/ABAGAIL

ef = FourPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)

rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200000)
fit.train()
print "RHC: " + str(ef.value(rhc.getOptimal()))

sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, 200000)
fit.train()
print "SA: " + str(ef.value(sa.getOptimal()))

ga = StandardGeneticAlgorithm(200, 100, 10, gap)
fit = FixedIterationTrainer(ga, 1000)
fit.train()
print "GA: " + str(ef.value(ga.getOptimal()))

mimic = MIMIC(200, 20, pop)
fit = FixedIterationTrainer(mimic, 1000)
fit.train()
print "MIMIC: " + str(ef.value(mimic.getOptimal()))

Exemple #3

Fichier : travelingsalesman.py Projet : onaclovtech/ABAGAIL

for x in range(0,N):
    path.append(rhc.getOptimal().getDiscrete(x))
print path

sa = SimulatedAnnealing(1E12, .999, hcp)
fit = FixedIterationTrainer(sa, 200000)
fit.train()
print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal()))
print "Route:"
path = []
for x in range(0,N):
    path.append(sa.getOptimal().getDiscrete(x))
print path


ga = StandardGeneticAlgorithm(2000, 1500, 250, gap)
fit = FixedIterationTrainer(ga, 1000)
fit.train()
print "GA Inverse of Distance: " + str(ef.value(ga.getOptimal()))
print "Route:"
path = []
for x in range(0,N):
    path.append(ga.getOptimal().getDiscrete(x))
print path


# for mimic we use a sort encoding
ef = TravelingSalesmanSortEvaluationFunction(points);
fill = [N] * N
ranges = list(array('i', fill))
odd = DiscreteUniformDistribution(ranges);