def run_four_peaks_exploringSA():
N=200
T=N/5
fill = [2] * N
ranges = array('i', fill)
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)
iters = [50, 100, 250, 500, 1000, 2500, 5000, 10000, 25000, 30000, 35000, 40000, 45000, 50000]
num_repeats = 5
all_sa_results = []
all_sa_times = []
coolings = [0.15, 0.35, 0.55, 0.75, 0.95]
for cooling in coolings:
sa_results = []
sa_times = []
for i in iters:
print(i)
for j in range(num_repeats):
start = time.time()
sa = SimulatedAnnealing(1E11, cooling, hcp)
fit = FixedIterationTrainer(sa, i)
fit.train()
end = time.time()
sa_results.append(ef.value(sa.getOptimal()))
sa_times.append(end - start)
print "SA cooling " + str(cooling) + ": " + str(ef.value(sa.getOptimal()))
all_sa_results.append(sa_results)
all_sa_results.append(sa_times)
with open('four_peaks_exploringSA.csv', 'w') as csvfile:
writer = csv.writer(csvfile)
for sa_results in all_sa_results:
writer.writerow(sa_results)
for sa_times in all_sa_times:
writer.writerow(sa_times)
return all_sa_results, all_sa_times
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) from time import time rhc = RandomizedHillClimbing(hcp) fit = FixedIterationTrainer(rhc, 600000) t0 = time() fit.train() print "RHC: " + str(ef.value(rhc.getOptimal())), "time taken", time() - t0 sa = SimulatedAnnealing(1E11, .95, hcp) fit = FixedIterationTrainer(sa, 600000) t0 = time() fit.train() print "SA: " + str(ef.value(sa.getOptimal())), "time taken", time() - t0 ga = StandardGeneticAlgorithm(200, 100, 10, gap) fit = FixedIterationTrainer(ga, 20000) t0 = time() fit.train() print "GA: " + str(ef.value(ga.getOptimal())), "time taken", time() - t0
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) start = time.time() rhc = RandomizedHillClimbing(hcp) fit = FixedIterationTrainer(rhc, 200000) fit.train() end = time.time() print "RHC optimum: " + str(ef.value(rhc.getOptimal())) print "RHC time: " + str(end - start) start = time.time() sa = SimulatedAnnealing(1E11, .95, hcp) fit = FixedIterationTrainer(sa, 200000) fit.train() end = time.time() print "SA optimum: " + str(ef.value(sa.getOptimal())) print "SA time: " + str(end - start) start = time.time() ga = StandardGeneticAlgorithm(200, 100, 10, gap) fit = FixedIterationTrainer(ga, 1000) fit.train() end = time.time()
def fourpeaksfunc(N, iterations):
rhcMult = 200
saMult = 200
gaMult = 2
mimicMult = 1
optimalOut = []
timeOut = []
evalsOut = []
T = N / 5
fill = [2] * N
ranges = array('i', fill)
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)
for niter in iterations:
iterOptimalOut = [N, niter]
iterTimeOut = [N, niter]
iterEvals = [N, niter]
start = time.time()
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, niter * rhcMult)
fit.train()
end = time.time()
rhcOptimal = ef.value(rhc.getOptimal())
rhcTime = end - start
print "RHC optimum: " + str(rhcOptimal)
print "RHC time: " + str(rhcTime)
iterOptimalOut.append(rhcOptimal)
iterTimeOut.append(rhcTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
sa = SimulatedAnnealing(1E20, .8, hcp)
fit = FixedIterationTrainer(sa, niter * saMult)
fit.train()
end = time.time()
saOptimal = ef.value(sa.getOptimal())
saTime = end - start
print "SA optimum: " + str(saOptimal)
print "SA time: " + str(saTime)
iterOptimalOut.append(saOptimal)
iterTimeOut.append(saTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
ga = StandardGeneticAlgorithm(200, 100, 10, gap)
fit = FixedIterationTrainer(ga, niter * gaMult)
fit.train()
end = time.time()
gaOptimal = ef.value(ga.getOptimal())
gaTime = end - start
print "GA optimum: " + str(gaOptimal)
print "GA time: " + str(gaTime)
iterOptimalOut.append(gaOptimal)
iterTimeOut.append(gaTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
mimic = MIMIC(200, 20, pop)
fit = FixedIterationTrainer(mimic, niter * mimicMult)
fit.train()
end = time.time()
mimicOptimal = ef.value(mimic.getOptimal())
mimicTime = end - start
print "MIMIC optimum: " + str(mimicOptimal)
print "MIMIC time: " + str(mimicTime)
iterOptimalOut.append(mimicOptimal)
iterTimeOut.append(mimicTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
optimalOut.append(iterOptimalOut)
timeOut.append(iterTimeOut)
evalsOut.append(iterEvals)
return [optimalOut, timeOut, evalsOut]
def main():
N=200
tempDenom = 5
T=N/tempDenom
fill = [2] * N
ranges = array('i', fill)
iterations = 2000
gaIters = 1000
mimicIters = 1000
gaPop = 200
gaMate = 100
gaMutate = 10
mimicSamples = 200
mimicToKeep = 20
saTemp = 1E11
saCooling = .95
alg = 'all'
run = 0
settings = []
try:
opts, args = getopt.getopt(sys.argv[1:], "ahn:rsgN:m:t:i:", ["gaIters=", "mimicIters=","gaPop=", "gaMate=", "gaMutate=", "mimicSamples=", "mimicToKeep=", "saTemp=", "saCooling="])
except:
print 'knapsack.py -i <iterations> -n <NUM_ITEMS> -c <COPIES_EACH> -w <MAX_WEIGHT> -v <MAX_VOLUME>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'knapsack.py -i <iterations> -n <NUM_ITEMS> -c <COPIES_EACH> -w <MAX_WEIGHT> -v <MAX_VOLUME>'
sys.exit(1)
elif opt == '-i':
iterations = int(arg)
elif opt == '-N':
N = int(arg)
elif opt == '-t':
T = float(arg)
elif opt == '-d':
tempDenom = int(arg)
elif opt == '-r':
alg = 'RHC'
elif opt == '-a':
alg = 'all'
elif opt == '-s':
alg = 'SA'
elif opt == '-g':
alg = 'GA'
elif opt == '-m':
alg = 'MIMIC'
elif opt == '--gaPop':
gaPop = int(arg)
elif opt == '--gaMate':
gaMate = int(arg)
elif opt == '--gaMutate':
gaMutate = int(arg)
elif opt == '--mimicSamples':
mimicSamples = int(arg)
elif opt == '--mimicToKeep':
mimicToKeep = int(arg)
elif opt == '--saTemp':
saTemp = float(arg)
elif opt == '--saCooling':
saCooling = float(arg)
elif opt == '--gaIters':
gaIters = int(arg)
elif opt == '--mimicIters':
mimicIters = int(arg)
elif opt == '-n':
run = int(arg)
vars = {
'N':N,
'tempDenom':tempDenom,
'T':T,
'fill':fill,
'ranges':ranges,
'iterations' :iterations,
'gaIters':gaIters,
'mimicIters':mimicIters,
'gaPop' :gaPop,
'gaMate' :gaMate,
'gaMutate' :gaMutate,
'mimicSamples' : mimicSamples,
'mimicToKeep' : mimicToKeep,
'saTemp' : saTemp,
'saCooling' : saCooling,
'alg' : alg,
'run' : run
}
settings = getSettings(alg, settings, vars)
T=N/tempDenom
fill = [2] * N
ranges = array('i', fill)
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)
if alg == 'RHC' or alg == 'all':
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iterations)
fit.train()
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(rhc.getOptimal()))
rows.append(row)
print "RHC: " + str(ef.value(rhc.getOptimal()))
output2('4Peaks', 'RHC', rows, settings)
rows = []
buildFooter("4Peaks", "RHC", rows, settings),
outputFooter("4Peaks", "RHC", rows, settings)
if alg == 'SA' or alg == 'all':
sa = SimulatedAnnealing(saTemp, saCooling, hcp)
fit = FixedIterationTrainer(sa, iterations)
fit.train()
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(sa.getOptimal()))
rows.append(row)
print "SA: " + str(ef.value(sa.getOptimal()))
output2('4Peaks', 'SA', rows, settings)
rows = []
buildFooter("4Peaks", "SA", rows, settings)
outputFooter("4Peaks", "SA", rows, settings)
if alg == 'GA' or alg == 'all':
ga = StandardGeneticAlgorithm(gaPop, gaMate, gaMutate, gap)
fit = FixedIterationTrainer(ga, gaIters)
fit.train()
print "GA: " + str(ef.value(ga.getOptimal()))
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(ga.getOptimal()))
rows.append(row)
output2('4Peaks', 'GA', rows, settings)
rows = []
buildFooter("4Peaks", "GA", rows, settings)
outputFooter("4Peaks", "GA", rows , settings)
if alg == 'MIMIC' or alg == 'all':
mimic = MIMIC(mimicSamples, mimicToKeep, pop)
fit = FixedIterationTrainer(mimic, mimicIters)
fit.train()
print "MIMIC: " + str(ef.value(mimic.getOptimal()))
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(mimic.getOptimal()))
rows.append(row)
output2('4Peaks', 'MIMIC', rows, settings)
rows = []
buildFooter("4Peaks", "GA", rows, settings)
outputFooter("4Peaks", "MIMIC", rows, settings)
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
iters_list = [100, 500, 1000, 2500, 5000, 7500, 10000, 20000]
print "Random Hill Climbing"
rhc = RandomizedHillClimbing(hcp)
for iters in iters_list:
fit = FixedIterationTrainer(rhc, iters)
start = time.time()
fit.train()
dur = time.time() - start
print "Iters: " + str(iters) + ", Fitness: " + str(
ef.value(rhc.getOptimal())) + ", Dur: " + str(dur)
print "Simulated Annealing"
temp = 100000
cooling_rate = 0.85
sa = SimulatedAnnealing(temp, 0.85, hcp)
for iters in iters_list:
fit = FixedIterationTrainer(sa, iters)
start = time.time()
fit.train()
dur = time.time() - start
print "Iters: " + str(iters) + ", Fitness: " + str(
ef.value(sa.getOptimal())) + ", Dur: " + str(dur)
print "Genetic Algorithm"
ga = StandardGeneticAlgorithm(2 * N, 300, 100, gap)
#N=200
T = N / 5
fill = [2] * N
ranges = array('i', fill)
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)
t0 = time.time()
calls = []
results = []
for _ in range(runs):
mimic = MIMIC(samples, tokeep, pop)
fit = FixedIterationTrainer(mimic, 1000)
fitness = fit.train()
results.append(ef.value(mimic.getOptimal()))
calls.append(ef.getTotalCalls())
ef.clearCount()
print "MIMIC, average results, " + str(sum(results) / float(runs))
print "MIMIC, average feval calls , " + str(sum(calls) / float(runs))
t1 = time.time() - t0
print "MIMIC, average time , " + str(t1 / float(runs))
sa_acc = []
ga_times = []
ga_acc = []
mimic_times = []
mimic_acc = []
NUMBER_ITERATIONS = 1000
for iteration in xrange(NUMBER_ITERATIONS):
if iteration % 10 == 0:
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iteration)
start = time.time()
fit.train()
end = time.time()
rhc_times.append(end - start)
rhc_acc.append(ef.value(rhc.getOptimal()))
print "RHC: " + str(ef.value(rhc.getOptimal()))
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, iteration)
start = time.time()
fit.train()
end = time.time()
sa_times.append(end - start)
sa_acc.append(ef.value(sa.getOptimal()))
print "SA: " + str(ef.value(sa.getOptimal()))
ga = StandardGeneticAlgorithm(200, 100, 10, gap)
fit = FixedIterationTrainer(ga, iteration)
start = time.time()
fit.train()
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)
t0 = time.time()
calls = []
results = []
for _ in range(runs):
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iters)
fitness = fit.train()
results.append(ef.value(rhc.getOptimal()))
calls.append(ef.getTotalCalls())
ef.clearCount()
print "RHC, average results , " + str(sum(results) / float(runs))
print "RHC, average feval calls , " + str(sum(calls) / float(runs))
t1 = time.time() - t0
print "RHC, average time , " + str(float(t1) / runs)
t0 = time.time()
calls = []
results = []
for _ in range(runs):
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, iters)
fitness = fit.train()
results.append(ef.value(sa.getOptimal()))
from time import time
f = open("experiments/results/fourpeaks_optimal_1000.txt", "w")
f.write("starting RHC\n")
rhc = RandomizedHillClimbing(hill_climbing_problem)
score = 0
iters = 0
t0 = time()
while iters < 60000:
score = rhc.train()
f.write(str(iters) + "," + str(score) + "\n")
iters += 1
print "RHC: " + str(ef.value(
rhc.getOptimal())), "time taken", time() - t0, "Iterations:", iters
f.write("starting SA\n")
sa = SimulatedAnnealing(1E13, .95, hill_climbing_problem)
t0 = time()
iters = 0
score = 0
while iters < 60000:
score = sa.train()
f.write(str(iters) + "," + str(score) + "\n")
iters += 1
print "SA: " + str(ef.value(
sa.getOptimal())), "time taken", time() - t0, "Iterations", iters
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
iterations = range(100, 50000, 100)
rhc_results = []
sa_results = []
ga_results = []
import time
rhc_startTime = time.time()
for iteration in iterations:
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iteration)
fit.train()
rhc_optimalVal = ef.value(rhc.getOptimal())
# print "RHC: " + str(rhc_optimalVal)
rhc_results.append(rhc_optimalVal)
rhc_endTime = time.time()
print "RHC Completed in: " + str(rhc_endTime - rhc_startTime)
sa_startTime = time.time()
for iteration in iterations:
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, iteration)
fit.train()
sa_optimalVal = ef.value(sa.getOptimal())
# print "SA: " + str(sa_optimalVal)
sa_results.append(sa_optimalVal)
sa_endTime = time.time()
print "SA Completed in: " + str(sa_endTime - sa_startTime)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
rhc = RandomizedHillClimbing(hcp)
sa = SimulatedAnnealing(1E11, .95, hcp)
ga = StandardGeneticAlgorithm(200, 100, 10, gap)
mimic = MIMIC(200, 20, pop)
rhc_f = open('out/op/fourpeaks/rhc.csv', 'w')
sa_f = open('out/op/fourpeaks/sa.csv', 'w')
ga_f = open('out/op/fourpeaks/ga.csv', 'w')
mimic_f = open('out/op/fourpeaks/mimic.csv', 'w')
for i in range(ITERATIONS):
rhc.train()
rhc_fitness = ef.value(rhc.getOptimal())
rhc_f.write('{},{}\n'.format(i, rhc_fitness))
sa.train()
sa_fitness = ef.value(sa.getOptimal())
sa_f.write('{},{}\n'.format(i, sa_fitness))
ga.train()
ga_fitness = ef.value(ga.getOptimal())
ga_f.write('{},{}\n'.format(i, ga_fitness))
mimic.train()
mimic_fitness = ef.value(mimic.getOptimal())
mimic_f.write('{},{}\n'.format(i, mimic_fitness))
rhc_f.close()
fill = [2] * N
ranges = array('i', fill)
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)
t0 = time.time()
calls = []
results = []
for _ in range(runs):
mimic = MIMIC(samples, tokeep, pop)
fit = FixedIterationTrainer(mimic, 1000)
fitness = fit.train()
results.append(ef.value(mimic.getOptimal()))
calls.append(ef.getTotalCalls())
ef.clearCount()
print "MIMIC, average results, " + str(sum(results)/float(runs))
print "MIMIC, average feval calls , " + str(sum(calls)/float(runs))
t1 = time.time() - t0
print "MIMIC, average time , " + str(t1/float(runs))
x = xrange(200, 3200, 200)
optimal_value = {'RHC': [], 'SA': [], 'GA': [], 'MIMIC': []}
for item in x:
stdout.write("\nRunning Four Peaks with %d iterations...\n" % item)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, item)
start = time.time()
fit.train()
end = time.time()
value = ef.value(rhc.getOptimal())
stdout.write("RHC took %0.03f seconds and found value %d\n" % (end -
start, value))
optimal_value['RHC'].append(value)
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, item)
start = time.time()
fit.train()
end = time.time()
value = ef.value(sa.getOptimal())
stdout.write("SA took %0.03f seconds and found value %d\n" % (end -
start, value))
optimal_value['SA'].append(value)
ga = StandardGeneticAlgorithm(200, 100, 20, gap)
runs = 10
# N=200
T = N / 5
fill = [2] * N
ranges = array("i", fill)
ef = FourPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
mf = DiscreteChangeOneMutation(ranges)
# print "Ga settings:\npop:%d\ncrossovertype:%d\ncrossoverrate:%d\nmutationrate:%d\n\n" % (ga_pop,co_type,ga_keep,ga_mut_type)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
t0 = time.time()
calls = []
results = []
for _ in range(runs):
# ga_pop = N*5
ga = StandardGeneticAlgorithm(ga_pop, ga_keep, ga_mut, gap)
fit = FixedIterationTrainer(ga, 1000)
fitness = fit.train()
results.append(ef.value(ga.getOptimal()))
calls.append(ef.getTotalCalls())
ef.clearCount()
print "GA, average results , " + str(sum(results) / float(runs))
print "GA, average feval calls , " + str(sum(calls) / float(runs))
t1 = time.time() - t0
print "GA, average time , " + str(t1 / float(runs))
runs = 10
#N=200
T = N / 5
fill = [2] * N
ranges = array('i', fill)
ef = FourPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
mf = DiscreteChangeOneMutation(ranges)
#print "Ga settings:\npop:%d\ncrossovertype:%d\ncrossoverrate:%d\nmutationrate:%d\n\n" % (ga_pop,co_type,ga_keep,ga_mut_type)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
t0 = time.time()
calls = []
results = []
for _ in range(runs):
#ga_pop = N*5
ga = StandardGeneticAlgorithm(ga_pop, ga_keep, ga_mut, gap)
fit = FixedIterationTrainer(ga, 1000)
fitness = fit.train()
results.append(ef.value(ga.getOptimal()))
calls.append(ef.getTotalCalls())
ef.clearCount()
print "GA, average results , " + str(sum(results) / float(runs))
print "GA, average feval calls , " + str(sum(calls) / float(runs))
t1 = time.time() - t0
print "GA, average time , " + str(t1 / float(runs))
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)
t0 = time.time()
calls = []
results = []
for _ in range(runs):
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iters)
fitness = fit.train()
results.append(ef.value(rhc.getOptimal()))
calls.append(ef.getTotalCalls())
ef.clearCount()
print "RHC, average results , " + str(sum(results)/float(runs))
print "RHC, average feval calls , " + str(sum(calls)/float(runs))
t1 = time.time() - t0
print "RHC, average time , " + str(float(t1)/runs)
t0 = time.time()
calls = []
results = []
for _ in range(runs):
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, iters)
fitness = fit.train()
ranges = array('i', fill)
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()))
def run_four_peaks():
N = 200
T = N / 5
fill = [2] * N
ranges = array('i', fill)
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)
iters = [50, 100, 250, 500, 1000, 2500, 5000, 10000, 25000, 50000, 100000]
num_repeats = 5
rhc_results = []
rhc_times = []
for i in iters:
print(i)
for j in range(num_repeats):
start = time.time()
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, i)
fit.train()
end = time.time()
rhc_results.append(ef.value(rhc.getOptimal()))
rhc_times.append(end - start)
print "RHC: " + str(ef.value(rhc.getOptimal()))
sa_results = []
sa_times = []
for i in iters:
print(i)
for j in range(num_repeats):
start = time.time()
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, i)
fit.train()
end = time.time()
sa_results.append(ef.value(sa.getOptimal()))
sa_times.append(end - start)
print "SA: " + str(ef.value(sa.getOptimal()))
ga_results = []
ga_times = []
for i in iters:
print(i)
for j in range(num_repeats):
start = time.time()
ga = StandardGeneticAlgorithm(200, 100, 10, gap)
fit = FixedIterationTrainer(ga, i)
fit.train()
end = time.time()
ga_results.append(ef.value(ga.getOptimal()))
ga_times.append(end - start)
print "GA: " + str(ef.value(ga.getOptimal()))
mimic_results = []
mimic_times = []
for i in iters[0:6]:
print(i)
for j in range(num_repeats):
start = time.time()
mimic = MIMIC(200, 20, pop)
fit = FixedIterationTrainer(mimic, i)
fit.train()
end = time.time()
mimic_results.append(ef.value(mimic.getOptimal()))
mimic_times.append(end - start)
print "MIMIC: " + str(ef.value(mimic.getOptimal()))
with open('four_peaks.csv', 'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(rhc_results)
writer.writerow(rhc_times)
writer.writerow(sa_results)
writer.writerow(sa_times)
writer.writerow(ga_results)
writer.writerow(ga_times)
writer.writerow(mimic_results)
writer.writerow(mimic_times)
return rhc_results, rhc_times, sa_results, sa_times, ga_results, ga_times, mimic_results, mimic_times
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
times = ""
print "RHC:"
for x in range(20):
start = time.time()
iterations = (x + 1) * 2500
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iterations)
fit.train()
print(str(ef.value(rhc.getOptimal())))
end = time.time()
times += "\n%0.03f" % (end - start)
print(times)
times = ""
print "SA:"
for x in range(20):
start = time.time()
iterations = (x + 1) * 2500
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, iterations)
fit.train()
print(str(ef.value(sa.getOptimal())))
end = time.time()
times += "\n%0.03f" % (end - start)
def run_four_peaks_experiments():
OUTPUT_DIRECTORY = './output'
if not os.path.exists(OUTPUT_DIRECTORY):
os.makedirs(OUTPUT_DIRECTORY)
N = 200
T = N / 5
fill = [2] * N
ranges = array('i', fill)
ef = FourPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
max_iter = 5000
outfile = OUTPUT_DIRECTORY + '/four_peaks_{}_log.csv'
# Randomized Hill Climber
filename = outfile.format('rhc')
with open(filename, 'w') as f:
f.write('iterations,fitness,time\n')
for it in range(0, max_iter, 10):
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, it)
start_time = time.clock()
fit.train()
elapsed_time = time.clock() - start_time
# fevals = ef.fevals
score = ef.value(rhc.getOptimal())
data = '{},{},{}\n'.format(it, score, elapsed_time)
print(data)
with open(filename, 'a') as f:
f.write(data)
# Simulated Annealing
filename = outfile.format('sa')
with open(filename, 'w') as f:
f.write('iteration,cooling_value,fitness,elapsed_time\n')
for cooling_value in (.19, .38, .76, .95):
for it in range(0, max_iter, 10):
sa = SimulatedAnnealing(1E11, cooling_value, hcp)
fit = FixedIterationTrainer(sa, it)
start_time = time.clock()
fit.train()
elapsed_time = time.clock() - start_time
# fevals = ef.fevalss
score = ef.value(sa.getOptimal())
data = '{},{},{},{}\n'.format(it, cooling_value, score,
elapsed_time)
print(data)
with open(filename, 'a') as f:
f.write(data)
# Genetic Algorithm
filename = outfile.format('ga')
with open(filename, 'w') as f:
f.write('iteration,population_size,to_mate,to_mutate,fitness,time\n')
for population_size, to_mate, to_mutate in itertools.product(
[200], [25, 50, 75, 100], [10, 20, 30, 40]):
for it in range(0, max_iter, 10):
ga = StandardGeneticAlgorithm(population_size, to_mate, to_mutate,
gap)
fit = FixedIterationTrainer(ga, it)
start_time = time.clock()
fit.train()
elapsed_time = time.clock() - start_time
# fevals = ef.fevals
score = ef.value(ga.getOptimal())
data = '{},{},{},{},{},{}\n'.format(it, population_size, to_mate,
to_mutate, score, elapsed_time)
print(data)
with open(filename, 'a') as f:
f.write(data)
# MIMIC
filename = outfile.format('mm')
with open(filename, 'w') as f:
f.write('iterations,samples,to_keep,fitness,time\n')
for samples, to_keep, m in itertools.product([200], [20],
[0.1, 0.3, 0.5, 0.7, 0.9]):
for it in range(0, 500, 10):
df = DiscreteDependencyTree(m, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
mm = MIMIC(samples, 20, pop)
fit = FixedIterationTrainer(mm, it)
start_time = time.clock()
fit.train()
elapsed_time = time.clock() - start_time
# fevals = ef.fevals
score = ef.value(mm.getOptimal())
data = '{},{},{},{},{},{}\n'.format(it, samples, to_keep, m, score,
elapsed_time)
print(data)
with open(filename, 'a') as f:
f.write(data)
#"""
#=======================
# Simulated Annealing
#=======================
print "Starting Simulated Annealing Seacrh..."
sa = SimulatedAnnealing(SA_start_temp, SA_temp_decay, hcp)
sa_iters = []
sa_fitness = []
sa_time = []
for i in maxiters_sa:
fit = FixedIterationTrainer(sa, i)
t1=time.time()
fit.train()
t2=time.time()
fitness = ef.value(sa.getOptimal())
time_ms=round(1000*(t2-t1),2)
sa_fitness.append(fitness)
sa_time.append(time_ms)
sa_iters.append(i)
print "SA fitness using "+ str(i)+" fixed iterations: " + str(fitness)
print "Time taken for SA using fixed iterations: "+str(time_ms)+" milliseconds"
print "Finished Simulated Annealing Seacrh."
print "="*100
#"""
"""
#=======================
# Genetic Algorithm
#=======================
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, N)
start = time.time()
fit.train()
end = time.time()
training_time = end - start
print "RHC: " + str(ef.value(rhc.getOptimal()))
OUTFILE = "%s%s.csv" % (OUTFILE_BASE, "RHC")
with open(OUTFILE, 'a+') as f:
f.write("%d,%f,%f\n" % (N, training_time, ef.value(rhc.getOptimal())))
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, N)
start = time.time()
fit.train()
end = time.time()
training_time = end - start
print "SA: " + str(ef.value(sa.getOptimal()))
OUTFILE = "%s%s.csv" % (OUTFILE_BASE, "SA")
with open(OUTFILE, 'a+') as f:
f.write("%d,%f,%f\n" % (N, training_time, ef.value(sa.getOptimal())))
