def RHC():
correctCount = 0
RHC_iters = 10
t=0
totalTime =0
totalIters = 0
global rhc
rhc = RandomizedHillClimbing(hcp)
while correctCount < NUM_RIGHT:
# print str(correctCount)+ " / 20 correct in RHC w/ iters " + str(RHC_iters)
fit = FixedIterationTrainer(rhc, RHC_iters)
start = time.time()
fitness = fit.train()
t = time.time() - start
totalIters+=RHC_iters
totalTime += t;
myWriter.addValue(fitness, "RHC_fitness", runNum)
myWriter.addValue(t, "RHC_searchTimes",runNum)
v = ef.value(rhc.getOptimal())
if v == N:
correctCount += 1
else:
correctCount = 0
#RHC_iters += 1
myWriter.addValue(totalTime,"RHC_times",runNum)
myWriter.addValue(totalIters,"RHC_iters",runNum)
print str(N) + ": RHC: " + str(ef.value(rhc.getOptimal()))+" took "+str(totalTime)+" seconds and " + str(totalIters) + " iterations"
def SA():
SA_iters = 10
correctCount = 0
t=0
totalTime=0
totalIters =0
global sa
sa = SimulatedAnnealing(1e11, .85, hcp)
while correctCount < NUM_RIGHT:
start = time.time()
fit = FixedIterationTrainer(sa, SA_iters)
fitness = fit.train()
t = time.time() - start
totalTime+=t
totalIters+= SA_iters
myWriter.addValue(fitness, "SA_fitness", runNum)
myWriter.addValue(t, "SA_searchTimes",runNum)
v = ef.value(sa.getOptimal())
if v == N:
correctCount += 1
else:
correctCount = 0
#SA_iters += 1
myWriter.addValue(t,"SA_times",0)
myWriter.addValue(int(SA_iters),"SA_iters",0)
print str(N) + ": SA: " + str(ef.value(sa.getOptimal())) + " took "+str(totalIters)+ " seconds and " + str(totalIters) + " iterations"
def MIMICtest():
correctCount = 0
MIMIC_iters = 10
MIMIC_samples = 5*N #max(1,int(N/10))
MIMIC_keep = int(.1 * MIMIC_samples)
t=0
while correctCount < NUM_RIGHT and MIMIC_iters <= 500:
MIMIC_keep = int( max(.1 * MIMIC_samples, 1))
mimic = MIMIC(int(MIMIC_samples), int(MIMIC_keep), pop)
start = time.time()
fit = FixedIterationTrainer(mimic, int(MIMIC_iters))
fitness = fit.train()
t = time.time() - start
v = ef.value(mimic.getOptimal())
myWriter.addValue(fitness, "MIMIC_fitness", runNum)
myWriter.addValue(t, "MIMIC_searchTimes",runNum)
if v==N:
correctCount +=1
else:
correctCount = 0
MIMIC_iters *=1.1
MIMIC_samples *=1.1
myWriter.addValue(t,"MIMIC_times",0)
myWriter.addValue(int(MIMIC_iters),"MIMIC_iters",0)
myWriter.addValue(int(MIMIC_samples),"MIMIC_samples",0)
myWriter.addValue(int(MIMIC_keep),"MIMIC_keep",0)
print(str(N) + ": MIMIC: " + str(ef.value(mimic.getOptimal())) + " took " + str(t) +
" seconds and " + str(int(MIMIC_iters)) + " iterations and " + str(int(MIMIC_samples)) +
" samples with keep " + str(int(MIMIC_keep)))
def mimicGATest():
popBegin = 1
popEnd = 101
keepBegin = 1
keepEnd = 90
mutBegin = 1
mutEnd = 90
itersBegin = 1
itersEnd = 200
samples = 10
keep = 2
problemSize = N
mimicRange = (problemSize)
iters = 1
paramRanges = Vector(8)
paramRanges.addElement(popBegin)
paramRanges.addElement(popEnd)
paramRanges.addElement(keepBegin)
paramRanges.addElement(keepEnd)
paramRanges.addElement(mutBegin)
paramRanges.addElement(mutEnd)
paramRanges.addElement(itersBegin)
paramRanges.addElement(itersEnd)
totalParamSize1 = (popEnd - popBegin +1) + (keepEnd - keepBegin +1) + (mutEnd - mutBegin +1) + (itersEnd - itersBegin +1)
allParamValues = range(popBegin, popEnd+1)+range(keepBegin, keepEnd+1)+range(mutBegin, mutEnd+1)+range(itersBegin, itersEnd+1)
totalParamSize = len(allParamValues)
metaFun = RamysEvalMetafunc(ranges)
discreteDist = RamysMimicDistribution(paramRanges) #DiscreteUniformDistribution(problemSize)
distFunc = DiscreteDependencyTree(.1, allParamValues)
findGA = GenericProbabilisticOptimizationProblem(metaFun, discreteDist, distFunc)
mimic = MIMIC(samples, keep, findGA)
fit = FixedIterationTrainer(mimic, iters)
fit.train()
print str(N) + ": MIMIC finds GA : " + str(ef.value(mimic.getOptimal()))
def main():
iterations = 200000
alg = 'all'
gaPop = 2000
gaMate = 1500
gaMutate = 250
mimicSamples = 500
mimicToKeep = 100
saTemp = 1E12
saCooling = .999
gaIters = 1000
mimicIters = 1000
run = 0
settings = []
try:
opts, args = getopt.getopt(sys.argv[1:], "ahrsgmn:i:", ["gaIters=", "mimicIters=", "gaPop=", "gaMate=", "gaMutate=", "mimicSamples=", "mimicToKeep=", "saTemp=", "saCooling="])
except:
print 'travelingsalesman.py -i <iterations>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'travelingsalesman.py -i <iterations>'
sys.exit(1)
elif opt == '-i':
if arg < 1:
print 'Iterations must be greater than 0'
sys.exit(2)
iterations = int(arg)
elif opt == '-a':
alg = 'all'
elif opt == '-r':
alg = 'RHC'
elif opt == '-s':
alg = 'SA'
elif opt == '-g':
alg = 'GA'
elif opt == '-m':
alg = 'MIMIC'
elif opt == '--gaPop':
if arg < 1:
print 'Population must be greater than 0'
sys.exit(2)
gaPop = int(arg)
elif opt == '--gaMate':
if arg < 1:
print 'Mating must be greater than 0'
sys.exit(2)
gaMate = int(arg)
elif opt == '--gaMutate':
if arg < 1:
print 'Mutators must be greater than 0'
sys.exit(2)
gaMutate = int(arg)
elif opt == '--mimicSamples':
if arg < 1:
print 'MIMIC samples must be greater than 0'
sys.exit(2)
mimicSamples = int(arg)
elif opt == '--mimicToKeep':
if arg < 1:
print 'MIMIC to keep must be greater than 0'
sys.exit(2)
mimicToKeep = int(arg)
elif opt == '--saTemp':
saTemp = float(arg)
elif opt == '--saCooling':
saCooling = float(arg)
elif opt == '-n':
run = int(arg)
elif opt == '--gaIters':
if arg < 1:
print 'GA Iterations must be greater than 0'
sys.exit(2)
gaIters = int(arg)
elif opt == '--mimicIters':
if arg < 1:
print 'MIMIC Iterations must be greater than 0'
sys.exit(2)
mimicIters = int(arg)
vars = {
'iterations' : iterations,
'alg' : alg,
'gaPop' : gaPop,
'gaMate' : gaMate,
'gaMutate' : gaMutate,
'mimicSamples' : mimicSamples,
'mimicToKeep' : mimicToKeep,
'saTemp' : saTemp,
'saCooling' : saCooling,
'gaIters' : gaIters,
'mimicIters' : mimicIters,
'run' : run
}
settings = getSettings(alg, settings, vars)
if gaPop < gaMate or gaPop < gaMutate or gaMate < gaMutate:
pebkac({gaPop: 'total population',gaMate : 'mating population', gaMutate : 'mutating population'}, alg, 'total population', settings)
if mimicSamples < mimicToKeep:
pebkac({mimicSamples: 'mimic samples', mimicToKeep : 'mimic to keep'}, alg, 'mimic samples', settings)
prob = 'Traveling Sales Problem'
invDist = {}
cities = CityList()
N = len(cities)
#random = Random()
points = [[0 for x in xrange(2)] for x in xrange(N)]
for i in range(0, len(points)):
coords = cities.getCoords(i)
points[i][0] = coords[0]
points[i][1] = coords[1]
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
rows = []
if alg == 'RHC' or alg == 'all':
print '\n----------------------------------'
print 'Using Random Hill Climbing'
for label, setting in settings:
print label + ":" + str(setting)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iterations)
fit.train()
path = []
for x in range(0,N):
path.append(rhc.getOptimal().getDiscrete(x))
output(prob, 'RHC', path, points, settings)
rows = []
row = []
row.append("Inverse of Distance")
row.append(ef.value(rhc.getOptimal()))
rows.append(row)
invDist['RHC'] = ef.value(rhc.getOptimal())
buildFooter(prob, 'RHC', rows, settings)
outputFooter(prob, 'RHC', rows, settings)
if alg == 'SA' or alg == 'all':
print 'Using Simulated Annealing'
for label, setting in settings:
print label + ":" + str(setting)
sa = SimulatedAnnealing(saTemp, saCooling, hcp)
fit = FixedIterationTrainer(sa, iterations)
fit.train()
path = []
for x in range(0,N):
path.append(sa.getOptimal().getDiscrete(x))
output(prob, 'SA', path, points, settings)
rows = []
row = []
row.append("Inverse of Distance")
row.append(ef.value(sa.getOptimal()))
rows.append(row)
invDist['SA'] = ef.value(sa.getOptimal())
buildFooter(prob, 'SA', rows, settings)
outputFooter(prob, 'SA', rows, settings)
if alg == 'GA' or alg == 'all':
print '\n----------------------------------'
print 'Using Genetic Algorithm'
for label, setting in settings:
print label + ":" + str(setting)
ga = StandardGeneticAlgorithm(gaPop, gaMate, gaMutate, gap)
fit = FixedIterationTrainer(ga, gaIters)
fit.train()
path = []
for x in range(0,N):
path.append(ga.getOptimal().getDiscrete(x))
output(prob, 'GA', path, points, settings)
rows = []
row = []
row.append("Inverse of Distance")
row.append(ef.value(ga.getOptimal()))
rows.append(row)
invDist['GA'] = ef.value(ga.getOptimal())
buildFooter(prob, 'GA', rows, settings)
outputFooter(prob, 'GA', rows, settings)
if alg == 'MIMIC' or alg == 'all':
print '\n----------------------------------'
print 'Using MIMIC'
for label, setting in settings:
print label + ":" + str(setting)
# for mimic we use a sort encoding
ef = TravelingSalesmanSortEvaluationFunction(points);
fill = [N] * N
ranges = array('i', fill)
odd = DiscreteUniformDistribution(ranges);
df = DiscreteDependencyTree(.1, ranges);
pop = GenericProbabilisticOptimizationProblem(ef, odd, df);
mimic = MIMIC(mimicSamples, mimicToKeep, pop)
fit = FixedIterationTrainer(mimic, mimicIters)
fit.train()
path = []
optimal = mimic.getOptimal()
fill = [0] * optimal.size()
ddata = array('d', fill)
for i in range(0,len(ddata)):
ddata[i] = optimal.getContinuous(i)
order = ABAGAILArrays.indices(optimal.size())
ABAGAILArrays.quicksort(ddata, order)
output(prob, 'MIMIC', order, points, settings)
rows = []
row = []
row.append("Inverse of Distance")
row.append(ef.value(mimic.getOptimal()))
rows.append(row)
invDist['MIMIC'] = ef.value(mimic.getOptimal())
buildFooter(prob, 'MIMIC', rows, settings)
outputFooter(prob, 'MIMIC', rows, settings)
maxn = max(len(key) for key in invDist)
maxd = max(len(str(invDist[key])) for key in invDist)
print "Results"
for result in invDist:
print "%-*s %s %-*s" % (len('Best Alg') + 2, result, ':', maxd, invDist[result])
if alg == 'all':
print "%-*s %s %-*s" % (len('Best Alg') + 2, 'Best Alg', ':', maxd, max(invDist.iterkeys(), key=(lambda key: invDist[key])))
print '----------------------------------'
runs : number of runs to average over
"""
fill = [2] * N
ranges = array('i', fill)
ef = CountOnesEvaluationFunction()
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, 100)
fitness = fit.train()
results.append(ef.value(mimic.getOptimal()))
calls.append(ef.getTotalCalls())
ef.clearCount()
print "MIMIC, average results, " + str(sum(results)/float(runs)) + ", countones_MIMIC-%d-%d-%d.txt" % (N, samples, tokeep)
print "MIMIC, average feval calls , " + str(sum(calls)/float(runs)) + ", countones_MIMIC-%d-%d-%d.txt" % (N, samples, tokeep)
t1 = time.time() - t0
print "MIMIC, average time , " + str(t1/float(runs)) + ", countones_MIMIC-%d-%d-%d.txt" % (N, samples, tokeep)
ef2 = TravelingSalesmanRouteEvaluationFunction(points)
fill = [N] * N
ranges = array('i', fill)
odd2 = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef2, odd2, df)
rhc = RandomizedHillClimbing(hcp)
sa = SimulatedAnnealing(SA_TEMPERATURE, SA_COOLING_FACTOR, hcp)
ga = StandardGeneticAlgorithm(GA_POPULATION, GA_CROSSOVER, GA_MUTATION,
gap)
mimic = MIMIC(MIMIC_SAMPLES, MIMIC_TO_KEEP, pop)
for n_iteration in iterations:
fit_rhc = FixedIterationTrainer(rhc, n_iteration * 200)
fit_sa = FixedIterationTrainer(sa, n_iteration * 200)
fit_ga = FixedIterationTrainer(ga, n_iteration)
fit_mimic = FixedIterationTrainer(mimic, n_iteration)
print("calculating the %d th iteration" % n_iteration)
# Training
start_rhc = time.time()
fit_rhc.train()
end_rhc = time.time()
start_sa = time.time()
fit_sa.train()
end_sa = time.time()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
# RHC
for t in range(numTrials):
fname = outfile.replace('@ALG@', 'RHC').replace('@N@', str(t + 1))
with open(fname, 'w') as f:
f.write('iterations,fitness,time,fevals\n')
ef = FlipFlopEvaluationFunction()
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 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(rhc.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
print st
with open(fname, 'a') as f:
f.write(st)
# SA
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):
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)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
ef = TravelingSalesmanSortEvaluationFunction(points);
fill = [N] * N
ranges = array('i', fill)
odd = DiscreteUniformDistribution(ranges);
df = DiscreteDependencyTree(.1, ranges);
pop = GenericProbabilisticOptimizationProblem(ef, odd, df);
start = time.time()
mimic = MIMIC(param[0], param[1], pop)
fit = FixedIterationTrainer(mimic, num_iterations)
fit.train()
value = str(ef.value(mimic.getOptimal()))
print "MIMIC Inverse of Distance: " + value
end = time.time()
print "Route:"
path = []
optimal = mimic.getOptimal()
fill = [0] * optimal.size()
ddata = array('d', fill)
for i in range(0, len(ddata)):
ddata[i] = optimal.getContinuous(i)
order = ABAGAILArrays.indices(optimal.size())
ABAGAILArrays.quicksort(ddata, order)
print order
end = time.time()
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)
def run_all_2(N=200,T=40,fout=None):
problem = 'flipflop'
# N=200
# T=N/10
maxEpochs = 10**5
maxTime = 300 #5 minutes
fill = [2] * N
ranges = array('i', fill)
# ef = FourPeaksEvaluationFunction(T)
ef = FlipFlopEvaluationFunction()
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
# mf = SwapMutation()
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
def run_algo(alg,fit,label,difficulty,iters):
trainTimes = [0.]
trainTime = []
scoreChange = [0.]
stuckCount = 10**3
prev = 0.
for epoch in range(0,maxEpochs,1):
st = time.clock()
fit.train()
et = time.clock()
trainTimes.append(trainTimes[-1]+(et-st))
trainTime.append((et-st))
rollingMean = 10
avgTime = (math.fsum(trainTime[-rollingMean:]) / float(rollingMean))
score = ef.value(alg.getOptimal())
# print(score,difficulty)
# trialString = '{}-{}-{}-{}'.format(label,score,epoch,trainTimes[-1])
trialData = [problem,difficulty,label,score,epoch,trainTimes[-1],avgTime,iters]
print(trialData)
# fout.writerow(trialData)
optimum = (difficulty-1)
if score >= optimum: break
scoreChange.append(abs(score-prev))
prev = score
scoreChange = scoreChange[-stuckCount:]
# print(scoreChange)
if max(scoreChange) == 0: break
if trainTimes[-1] > maxTime: break
# print(trialData)
fout.writerow(trialData)
iters = 10
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iters)
run_algo(rhc,fit,'RHC',N,iters)
iters = 10
startTemp = 1E10
coolingFactor = .99
sa = SimulatedAnnealing(startTemp, coolingFactor, hcp)
fit = FixedIterationTrainer(sa, iters)
run_algo(sa,fit,'SA',N,iters)
iters = 10
population = 300
mates = 40
mutations = 10
ga = StandardGeneticAlgorithm(population, mates, mutations, gap)
fit = FixedIterationTrainer(ga, iters)
run_algo(ga,fit,'GA',N,iters)
iters = 10
samples = 200
keep = 5
mimic = MIMIC(samples, keep, pop)
fit = FixedIterationTrainer(mimic, iters)
run_algo(mimic,fit,'MIMIC',N,iters)
n_iteration = 5000
sa_fitness = [[] for i in range(cycle)]
sa_training_time = [[] for i in range(cycle)]
for n in range(cycle):
print("the %d th cycle" % (n + 1))
ef = ContinuousPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
for SA_TEMPERATURE in SA_TEMPERATURE_pool:
sa = SimulatedAnnealing(SA_TEMPERATURE, SA_COOLING_FACTOR, hcp)
fit_sa = FixedIterationTrainer(sa, n_iteration)
print("calculating for temperature = %e" % SA_TEMPERATURE)
# Training
start_sa = time.time()
fit_sa.train()
end_sa = time.time()
# Result extracting
last_training_time_sa = end_sa - start_sa
sa_training_time[n].append(last_training_time_sa)
sa_fitness[n].append(ef.value(sa.getOptimal()))
overall_sa_training_time = list_avg(*sa_training_time)
overall_sa_fitness = list_avg(*sa_fitness)
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)
# Algorithm declaration
rhc = RandomizedHillClimbing(hcp)
sa = SimulatedAnnealing(SA_TEMPERATURE, SA_COOLING_FACTOR, hcp)
ga = StandardGeneticAlgorithm(GA_POPULATION, GA_CROSSOVER, GA_MUTATION,
gap)
mimic = MIMIC(MIMIC_SAMPLES, MIMIC_TO_KEEP, pop)
# Trainer declaration
fit_rhc = FixedIterationTrainer(rhc, current_iteration_count)
fit_sa = FixedIterationTrainer(sa, current_iteration_count)
fit_ga = FixedIterationTrainer(ga, current_iteration_count)
fit_mimic = FixedIterationTrainer(mimic, current_iteration_count)
print("Computing for %d iterations" % current_iteration_count)
# Fitting
start_rhc = time.time()
fit_rhc.train()
end_rhc = time.time()
start_sa = time.time()
fit_sa.train()
end_sa = time.time()
rhc_total = 0
rhc_time = 0
for x in range(runs):
ranges = array('i', [2] * N)
fitness = ContinuousPeaksEvaluationFunction(T)
discrete_dist = DiscreteUniformDistribution(ranges)
discrete_neighbor = DiscreteChangeOneNeighbor(ranges)
discrete_mutation = DiscreteChangeOneMutation(ranges)
crossover = SCO()
discrete_dependency = DiscreteDependencyTree(.1, ranges)
hill_problem = GHC(fitness, discrete_dist, discrete_neighbor)
start = time.clock()
rhc_problem = RHC(hill_problem)
fit = FixedIterationTrainer(rhc_problem, iteration)
fit.train()
end = time.clock()
full_time = end - start
rhc_total += fitness.value(rhc_problem.getOptimal())
rhc_time += full_time
rhc_total_avg = rhc_total / runs
rhc_time_avg = rhc_time / runs
data = '{},{},{}\n'.format(iteration, rhc_total_avg, rhc_time_avg)
print(data)
with open(output_directory, 'a') as f:
f.write(data)
#SA
def rhc_fac(args = {}):
constant_params = {'hcp':hcp}
params = merge_two_dicts(args, constant_params)
print(params)
rhc = RandomizedHillClimbing(hcp)
return FixedIterationTrainer(rhc, num_iterations)
def ga_fac(args = {}):
constant_params = {'hcp':hcp}
params = merge_two_dicts(args,constant_params)
ga = StandardGeneticAlgorithm(args['populationSize'], int(args['populationSize'] * args['toMate']), int(args['populationSize'] * args['toMutate']), gap)
gfit = FixedIterationTrainer(ga, num_iterations)
return gfit
ef = CountOnesEvaluationFunction()
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):
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200)
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, 200)
# fevals = ef.fevals
score = ef.value(ga.getOptimal())
# ef.fevals -= 1
st = '{},{},{}\n'.format(i,score,times[-1])
print st
with open(fname,'a') as f:
f.write(st)
'''
#MIMIC
for t in range(numTrials):
for samples, keep, m in product([100], [50], [0.1, 0.3, 0.5, 0.7, 0.9]):
fname = outfile.replace('@ALG@', 'MIMIC{}_{}_{}'.format(
samples, keep, m)).replace('@N@', str(t + 1))
with open(fname, 'w') as f:
f.write('iterations,fitness,time,fevals\n')
mimic = MIMIC(samples, keep, pop)
fit = FixedIterationTrainer(mimic, 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(mimic.getOptimal())
# ef.fevals -= 1
st = '{},{},{}\n'.format(i, score, times[-1])
print st
with open(fname, 'a') as f:
f.write(st)
# print "Finished Simulated Annealing Seacrh."
# print "="*100
#"""
#=======================
# Genetic Algorithm
#=======================
print "Starting Genetic Algorithm Seacrh..."
ga = StandardGeneticAlgorithm(GA_popsize, GA_toMate, GA_toMutate, gap)
ga_iters = []
ga_fitness = []
ga_time = []
for i in maxiters_ga:
fit = FixedIterationTrainer(ga, i)
t1 = time.time()
fit.train()
t2 = time.time()
fitness = ef.value(ga.getOptimal())
time_ms = round(1000 * (t2 - t1), 2)
ga_fitness.append(fitness)
ga_time.append(time_ms)
ga_iters.append(i)
print "GA fitness using " + str(i) + " fixed iterations: " + str(fitness)
print "Time taken for GA using fixed iterations: " + str(
time_ms) + " milliseconds"
print "Finished Genetic Algorithm Seacrh."
print "=" * 100
"""
# RHC
#############################################################
fname = outfile.replace('@ALG@', 'RHC')
with open(fname, 'w') as f:
f.write('N,T,trial,iterations,fitness,time,fevals\n')
for N in range(N_min, N_max + 1, N_min):
fill = [2] * N
ranges = array('i', fill)
T = 4 * N / 10
for t in range(numTrials):
ef = ContinuousPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, increments)
times = [0]
lastScore = 0
halt_count = 0
total_iter = 0
for i in range(0, maxIters, increments):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(rhc.getOptimal())
ef.fevals -= 1
st = '{},{},{},{},{},{},{},{}\n'.format(N, T, t, i, score, times[-1], fevals, halt_count)
print st
with open(fname, 'a') as f:
ef = MaxKColorFitnessFunction(vertices)
odd = DiscretePermutationDistribution(K)
nf = SwapNeighbor()
mf = SwapMutation()
cf = SingleCrossOver()
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
df = DiscreteDependencyTree(.1)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
start_time = time.time()
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 20000)
fit.train()
value = ef.value(rhc.getOptimal())
stdout.write("RHC %s Value: %d. Time: %0.03f\n" % (ef.foundConflict(),
value, time.time() - start_time))
data['RHC'][0] += value
if ef.foundConflict() == "Found Max-K Color Combination !":
data['RHC'][1] += 1
start_time = time.time()
sa = SimulatedAnnealing(1E12, .1, hcp)
fit = FixedIterationTrainer(sa, 20000)
fit.train()
value = ef.value(sa.getOptimal())
stdout.write("SA %s Value: %d. Time: %0.03f\n" % (ef.foundConflict(),
def travelingsalesmanfunc(N, iterations):
rhcMult = 1500
saMult = 1500
gaMult = 1
mimicMult = 3
random = Random()
points = [[0 for x in xrange(2)] for x in xrange(N)]
for i in range(0, len(points)):
points[i][0] = random.nextDouble()
points[i][1] = random.nextDouble()
optimalOut = []
timeOut = []
evalsOut = []
for niter in iterations:
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
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 Inverse of Distance: optimum: " + str(rhcOptimal)
print "RHC time: " + str(rhcTime)
#print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
print "Route:"
path = []
for x in range(0, N):
path.append(rhc.getOptimal().getDiscrete(x))
print path
iterOptimalOut.append(rhcOptimal)
iterTimeOut.append(rhcTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
sa = SimulatedAnnealing(1E12, .999, hcp)
fit = FixedIterationTrainer(sa, niter * saMult)
fit.train()
end = time.time()
saOptimal = ef.value(sa.getOptimal())
saTime = end - start
print "SA Inverse of Distance optimum: " + str(saOptimal)
print "SA time: " + str(saTime)
#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
iterOptimalOut.append(saOptimal)
iterTimeOut.append(saTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
ga = StandardGeneticAlgorithm(2000, 1500, 250, gap)
fit = FixedIterationTrainer(ga, niter * gaMult)
fit.train()
end = time.time()
gaOptimal = ef.value(ga.getOptimal())
gaTime = end - start
print "GA Inverse of Distance optimum: " + str(gaOptimal)
print "GA time: " + str(gaTime)
#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
iterOptimalOut.append(gaOptimal)
iterTimeOut.append(gaTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
# for mimic we use a sort encoding
ef = TravelingSalesmanSortEvaluationFunction(points)
fill = [N] * N
ranges = array('i', fill)
odd = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
start = time.time()
mimic = MIMIC(500, 100, pop)
fit = FixedIterationTrainer(mimic, niter * mimicMult)
fit.train()
end = time.time()
mimicOptimal = ef.value(mimic.getOptimal())
mimicTime = end - start
print "MIMIC Inverse of Distance optimum: " + str(mimicOptimal)
print "MIMIC time: " + str(mimicTime)
#print "MIMIC Inverse of Distance: " + str(ef.value(mimic.getOptimal()))
print "Route:"
path = []
optimal = mimic.getOptimal()
fill = [0] * optimal.size()
ddata = array('d', fill)
for i in range(0, len(ddata)):
ddata[i] = optimal.getContinuous(i)
order = ABAGAILArrays.indices(optimal.size())
ABAGAILArrays.quicksort(ddata, order)
print order
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]
st = '{},{},{}\n'.format(i,score,times[-1])
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.replace('@ALG@',
'SA{}'.format(CE)).replace('@N@', str(t + 1))
with open(fname, 'w') as f:
f.write('iterations,fitness,time,fevals\n')
ef = TravelingSalesmanRouteEvaluationFunction(points)
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)
score = ef.value(sa.getOptimal())
st = '{},{},{}\n'.format(i, score, times[-1])
print st
with open(fname, 'a') as f:
f.write(st)
'''
#GA
for t in range(numTrials):
for pop,mate,mutate in product([100],[50,30,10],[30,10]):
# create range
fill = [COPIES_EACH + 1] * N
ranges = array('i', fill)
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)
# -- begin problem
t0 = time.time()
calls = []
results = []
for _ in range(runs):
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))
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
volumes = array('d', fill)
for i in range(0, NUM_ITEMS):
weights[i] = random.nextDouble() * MAX_WEIGHT
volumes[i] = random.nextDouble() * MAX_VOLUME
fill = [COPIES_EACH + 1] * NUM_ITEMS
ranges = array('i', fill)
ef = KnapsackEvaluationFunction(weights, volumes, KNAPSACK_VOLUME, copies)
odd = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
for MIMIC_SAMPLES in MIMIC_SAMPLES_pool:
mimic = MIMIC(MIMIC_SAMPLES, MIMIC_TO_KEEP, pop)
fit_mimic = FixedIterationTrainer(mimic, n_iteration)
print("calculating for MIMIC_SAMPLES = %d" % MIMIC_SAMPLES)
# Training
start_mimic = time.time()
fit_mimic.train()
end_mimic = time.time()
# Result extracting
last_training_time_mimic = end_mimic - start_mimic
mimic_training_time[n].append(last_training_time_mimic)
mimic_fitness[n].append(ef.value(mimic.getOptimal()))
overall_mimic_training_time = list_avg(*mimic_training_time)
overall_mimic_fitness = list_avg(*mimic_fitness)
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)
print("N: " + str(N))
ef = CountOnesEvaluationFunction()
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)
rhcIterations = 100
fit = FixedIterationTrainer(rhc, rhcIterations)
fit.train()
print("\nRHC: " + str(ef.value(rhc.getOptimal())))
print("RHC Iterations: " + str(rhcIterations))
end = time.time()
traintime = end - start
print("RHC results time: %0.03f seconds" % (traintime,))
start = time.time()
sa = SimulatedAnnealing(100, .95, hcp)
saIterations = 200
fit = FixedIterationTrainer(sa, saIterations)
fit.train()
print("\nSA: " + str(ef.value(sa.getOptimal())))
print("SA Iterations: " + str(saIterations))
end = time.time()
fill = [COPIES_EACH + 1] * NUM_ITEMS
ranges = array('i', fill)
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)
expt = "expt_avg"
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200000)
score_RHC.append(train(rhc, "RHC", ef, 200000, "test", expt))
print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
sa = SimulatedAnnealing(1E9, .95, hcp)
score_SA.append(train(sa, "SA", ef, 200000, "test", expt))
print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal()))
ga = StandardGeneticAlgorithm(300, 80, 5, gap)
score_GA.append(train(ga, "GA", ef, 40000, "test", expt))
print "GA Inverse of Distance: " + str(ef.value(ga.getOptimal()))
mimic = MIMIC(250, 10, pop)
score_MIMIC.append(train(mimic, "MIMIC", ef, 4000, "test", expt))
print "MIMIC Inverse of Distance: " + str(ef.value(mimic.getOptimal()))
# fit = FixedIterationTrainer(sa, it_count)
# fit.train()
# value = ef.value(sa.getOptimal())
# print "SA: " + str(ef.value(sa.getOptimal()))
# ga = StandardGeneticAlgorithm(param[0], param[1], param[2], gap)
# fit = FixedIterationTrainer(ga, it_count)
# fit.train()
# value = ef.value(ga.getOptimal())
# # print "GA: " + str(ef.value(ga.getOptimal()))
start = time.time()
mimic = MIMIC(param[0], param[1], pop)
fit = FixedIterationTrainer(mimic, it_count)
fit.train()
value = ef.value(mimic.getOptimal())
# print "MIMIC: " + str(ef.value(mimic.getOptimal()))
end = time.time()
results = {
'num_iterations': it_count,
'value': value,
'time': end - start
}
print 'MIMIC', param, results
writer.writerow(results)
csv_file.close()
print '------'
print '***** ***** ***** ***** *****'
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, num_iterations)
# fit.train()
start = time.time()
sa = SimulatedAnnealing(param[0], param[1], hcp)
fit = FixedIterationTrainer(sa, num_iterations)
fit.train()
end = time.time()
value = str(ef.value(sa.getOptimal()))
results = {
'num_iterations': num_iterations,
'value': value,
'time': end - start
}
print 'SA', param, results
writer.writerow(results)
# ga = StandardGeneticAlgorithm(param[0], param[1], param[2], gap)
# fit = FixedIterationTrainer(ga, num_iterations)
# fit.train()
# print "GA: " + str(ef.value(ga.getOptimal()))
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
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)
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 run_knapsack_experiments():
OUTPUT_DIRECTORY = './output'
# Random number generator */
random = Random()
# The number of items
NUM_ITEMS = 40
# The number of copies each
COPIES_EACH = 4
# The maximum weight for a single element
MAX_WEIGHT = 50
# The maximum volume for a single element
MAX_VOLUME = 50
# The volume of the knapsack
KNAPSACK_VOLUME = MAX_VOLUME * NUM_ITEMS * COPIES_EACH * .4
# create copies
fill = [COPIES_EACH] * NUM_ITEMS
copies = array('i', fill)
# create weights and volumes
fill = [0] * NUM_ITEMS
weights = array('d', fill)
volumes = array('d', fill)
for i in range(0, NUM_ITEMS):
weights[i] = random.nextDouble() * MAX_WEIGHT
volumes[i] = random.nextDouble() * MAX_VOLUME
# create range
fill = [COPIES_EACH + 1] * NUM_ITEMS
ranges = array('i', fill)
ef = KnapsackEvaluationFunction(weights, volumes, KNAPSACK_VOLUME, copies)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = UniformCrossOver()
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
max_iter = 5000
outfile = OUTPUT_DIRECTORY + '/knapsack_{}_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,time\n')
for cooling_value in (.19, .38, .76, .95):
for it in range(0, max_iter, 10):
sa = SimulatedAnnealing(200, cooling_value, hcp)
fit = FixedIterationTrainer(sa, it)
start_time = time.clock()
fit.train()
elapsed_time = time.clock() - start_time
# fevals = ef.fevals
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], [110, 120, 130, 140, 150], [2, 4, 6, 8]):
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,m,fitness,time\n')
for samples, to_keep, m in itertools.product([200], [100],
[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)
# learn weigths with back propagation network_bp = factory.createClassificationNetwork([inputLayer, hiddenLayer, outputLayer]) bp = BatchBackPropagationTrainer(set, network_bp, measure, RPROPUpdateRule()) cvt = ConvergenceTrainer(bp) cvt.train() print "\nBP training error:", errorRate(network_bp, train) print "BP training confusion matrix:", confusionMatrix(network_bp, train) print " BP test error:", errorRate(network_bp, test) print " BP test confusion matrix:", confusionMatrix(network_bp, test) # learn weights with randomized hill climbing network_rhc = factory.createClassificationNetwork([inputLayer, hiddenLayer, outputLayer]) nnop_rhc = NeuralNetworkOptimizationProblem(set, network_rhc, measure) rhc = RandomizedHillClimbing(nnop_rhc) fit = FixedIterationTrainer(rhc, it_rhc) fit.train() op = rhc.getOptimal(); network_rhc.setWeights(op.getData()) print "\nRHC training error:", errorRate(network_rhc, train) print "RHC training confusion matrix:", confusionMatrix(network_rhc, train) print " RHC test error:", errorRate(network_rhc, test) print " RHC test confusion matrix:", confusionMatrix(network_rhc, test) # learn weights with simulated annealing network_sa = factory.createClassificationNetwork([inputLayer, hiddenLayer, outputLayer]) nnop_sa = NeuralNetworkOptimizationProblem(set, network_sa, measure) sa = SimulatedAnnealing(1E11, 0.95, nnop_sa) fit = FixedIterationTrainer(sa, it_sa) fit.train() op = sa.getOptimal();
# fit = FixedIterationTrainer(rhc, it_count)
# fit.train()
# print "Time -->", end - start
# print "RHC: " + str(value)
# sa = SimulatedAnnealing(param[0], param[1], hcp)
# fit = FixedIterationTrainer(sa, it_count)
# fit.train()
# value = ef.value(sa.getOptimal())
# print "SA: " + str(ef.value(sa.getOptimal()))
start = time.time()
ga = StandardGeneticAlgorithm(param[0], param[1], param[2], gap)
fit = FixedIterationTrainer(ga, it_count)
fit.train()
value = ef.value(ga.getOptimal())
# print "GA: " + str(ef.value(ga.getOptimal()))
end = time.time()
results = {
'num_iterations': it_count,
'value': value,
'time': end - start
}
print 'GA', param, results
writer.writerow(results)
# mimic = MIMIC(200, 20, pop)
# fit = FixedIterationTrainer(mimic, it_count)
# fit.train()
# print "MIMIC: " + str(ef.value(mimic.getOptimal()))
def main():
# The number of items
NUM_ITEMS = 40
# The number of copies each
COPIES_EACH = 4
# The maximum weight for a single element
MAX_WEIGHT = 50
# The maximum volume for a single element
MAX_VOLUME = 50
iterations = 20000
gaIters = 1000
mimicIters = 1000
gaPop = 200
gaMate = 150
gaMutate = 25
mimicSamples = 200
mimicToKeep = 100
saTemp = 100
saCooling = .95
alg = 'all'
run = 0
settings = []
try:
opts, args = getopt.getopt(sys.argv[1:], "ahrsgmn:N:c:w:v: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':
NUM_ITEMS = int(arg)
elif opt == '-c':
COPIES_EACH = int(arg)
elif opt == '-w':
MAX_WEIGHT = int(arg)
elif opt == '-v':
MAX_VOLUME = int(arg)
elif opt == '-n':
run = int(arg)
elif opt == '-r':
alg = 'RHC'
elif opt == '-s':
alg = 'SA'
elif opt == '-g':
alg = 'GA'
elif opt == '-m':
alg = 'MIMIC'
elif opt == '-a':
alg = 'all'
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)
vars ={
'NUM_ITEMS' : NUM_ITEMS,
'COPIES_EACH' : COPIES_EACH,
'MAX_WEIGHT' : MAX_WEIGHT,
'MAX_VOLUME' : MAX_VOLUME,
'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)
# Random number generator */
random = Random()
# The volume of the knapsack
KNAPSACK_VOLUME = MAX_VOLUME * NUM_ITEMS * COPIES_EACH * .4
# create copies
fill = [COPIES_EACH] * NUM_ITEMS
copies = array('i', fill)
# create weights and volumes
fill = [0] * NUM_ITEMS
weights = array('d', fill)
volumes = array('d', fill)
for i in range(0, NUM_ITEMS):
weights[i] = random.nextDouble() * MAX_WEIGHT
volumes[i] = random.nextDouble() * MAX_VOLUME
# create range
fill = [COPIES_EACH + 1] * NUM_ITEMS
ranges = array('i', fill)
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)
if alg == 'RHC' or alg == 'all':
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iterations)
fit.train()
print "RHC: " + str(ef.value(rhc.getOptimal()))
rows = []
row = []
row.append("Evaluation Function Value")
row.append(str(ef.value(rhc.getOptimal())))
rows.append(row)
output2('Knapsack', 'RHC', rows, settings)
rows = []
buildFooter("Knapsack", "RHC", rows, settings)
outputFooter("Knapsack", "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('Knapsack', 'SA', rows, settings)
rows = []
buildFooter("Knapsack", "SA", rows, settings)
outputFooter("Knapsack", "SA", rows, settings)
if alg == 'GA' or alg == 'all':
ga = StandardGeneticAlgorithm(gaPop, gaMate, gaMutate, gap)
fit = FixedIterationTrainer(ga, gaIters)
fit.train()
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(ga.getOptimal()))
rows.append(row)
print "GA: " + str(ef.value(ga.getOptimal()))
output2('Knapsack', 'GA', rows, settings)
buildFooter("Knapsack", "GA", rows, settings)
outputFooter("Knapsack", "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('Knapsack', 'MIMIC', rows, settings)
rows = []
buildFooter("Knapsack", "MIMIC", rows, settings)
outputFooter("Knapsack", "MIMIC", rows , settings)
points[i][1] = random.nextDouble()
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
iters_list = [100, 500, 1000, 2500, 5000, 7500, 10000, 20000]
print "Random Hill Climb"
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 "Route:"
# path = []
# for x in range(0,N):
# path.append(rhc.getOptimal().getDiscrete(x))
# print path
print "Simulated Annealing"
# 1e13, 0.8, 1e12 0.85, ... dang
temp = 1E13
cooling_rate = 0.90
