st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
print st
with open(fname, 'a') as f:
f.write(st)
# SA
for t in range(numTrials):
for CE in [0.15, 0.35, 0.55, 0.75, 0.95]:
fname = outfile.format('SA{}'.format(CE), str(t + 1))
with open(fname, 'w') as f:
f.write('iterations,fitness,time,fevals\n')
ef = TwoColorsEvaluationFunction()
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
sa = SimulatedAnnealing(1E10, CE, hcp)
fit = FixedIterationTrainer(sa, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(sa.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
print st
with open(fname, 'a') as f:
f.write(st)
ef = KnapsackEvaluationFunction(weights, volumes, KNAPSACK_VOLUME, copies) odd = DiscreteUniformDistribution(ranges) nf = DiscreteChangeOneNeighbor(ranges) mf = DiscreteChangeOneMutation(ranges) cf = UniformCrossOver() 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(100, .95, hcp) fit = FixedIterationTrainer(sa, 200000) fit.train() print "SA: " + str(ef.value(sa.getOptimal())) ga = StandardGeneticAlgorithm(200, 150, 25, gap) fit = FixedIterationTrainer(ga, 1000) fit.train() print "GA: " + str(ef.value(ga.getOptimal())) mimic = MIMIC(200, 100, pop) fit = FixedIterationTrainer(mimic, 1000) fit.train() print "MIMIC: " + str(ef.value(mimic.getOptimal()))
# print("Early stopping at iteration {}".format(i))
# break
# else:
# early_stop.pop(0)
# SA
for t in range(numTrials):
for T in [1e1, 1e3, 1e5, 1e7, 1e9, 1e11, 1e13]:
for CE in [0.20, 0.40, 0.60, 0.80, 0.90, 0.99]:
fname = outfile.replace(
'@ALG@',
'SA_CE{}_T{}'.format(int(CE * 100),
int(T))).replace('@N@', str(t + 1))
with open(fname, 'w') as f:
f.write('iterations,fitness,time\n')
sa = SimulatedAnnealing(T, CE, hcp)
fit = FixedIterationTrainer(sa, 10)
times = [0]
early_stop = []
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
score = ef.value(sa.getOptimal())
st = '{},{},{}\n'.format(i, score, times[-1])
print st
with open(fname, 'a') as f:
f.write(st)