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()))
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)
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 '----------------------------------'
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)
ga = StandardGeneticAlgorithm(500, 100, 10, genetic_problem)
t0 = time()
iters = 0
score = 0
f.write("starting GA\n")
while iters < 30000:
ga.train()
score = ef.value(ga.getOptimal())
f.write(str(iters) + "," + str(score) + "\n")
iters += 1
print "GA: " + str(ef.value(
ga.getOptimal())), "time taken", time() - t0, "Iterations", iters
mimic = MIMIC(100, 50, probablistic_optimization)
score = 0
t0 = time()
iters = 0
f.write("starting MIMIC\n")
while iters < 100:
mimic.train()
score = ef.value(mimic.getOptimal())
print iters, score
f.write(str(iters) + "," + str(score) + "\n")
iters += 1
print "MIMIC: " + str(ef.value(
mimic.getOptimal())), "time taken", time() - t0, "Iterations", iters
ga = StandardGeneticAlgorithm(200, 150, 25, gap)
fit = FixedIterationTrainer(ga, iteration)
start = time.time()
fit.train()
end = time.time()
ga_times.append(end - start)
ga_acc.append(ef.value(ga.getOptimal()))
print "GA: " + str(ef.value(ga.getOptimal()))
mimic = MIMIC(200, 100, pop)
fit = FixedIterationTrainer(mimic, iteration)
start = time.time()
fit.train()
end = time.time()
mimic_times.append(end - start)
mimic_acc.append(ef.value(mimic.getOptimal()))
print "MIMIC: " + str(ef.value(mimic.getOptimal()))
else:
continue
metrics = [
rhc_acc,
rhc_times,
sa_acc,
sa_times,
ga_acc,
ga_times,
mimic_acc,
mimic_times,
]
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()))
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())))
ga = StandardGeneticAlgorithm(200, 100, 10, gap)
fit = FixedIterationTrainer(ga, N)
start = time.time()
fit.train()
end = time.time()
training_time = end - start
print "GA: " + str(ef.value(ga.getOptimal()))
OUTFILE = "%s%s.csv" % (OUTFILE_BASE, "GA")
with open(OUTFILE, 'a+') as f:
f.write("%d,%f,%f\n" % (N, training_time, ef.value(ga.getOptimal())))
mimic = MIMIC(200, 20, pop)
fit = FixedIterationTrainer(mimic, N)
start = time.time()
fit.train()
end = time.time()
training_time = end - start
print "MIMIC: " + str(ef.value(mimic.getOptimal()))
OUTFILE = "%s%s.csv" % (OUTFILE_BASE, "MIMIC")
with open(OUTFILE, 'a+') as f:
f.write("%d,%f,%f\n" %
(N, training_time, ef.value(mimic.getOptimal())))
fit = FixedIterationTrainer(ga, N)
start = time.time()
fit.train()
end = time.time()
training_time = end - start
print "GA Inverse of Distance: " + str(ef.value(ga.getOptimal()))
OUTFILE = "%s%s.csv" % (OUTFILE_BASE, "GA")
with open(OUTFILE, 'a+') as f:
f.write("%d,%f,%f\n" % (N, training_time, ef.value(ga.getOptimal())))
# 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(500, 100, pop)
fit = FixedIterationTrainer(mimic, N)
start = time.time()
fit.train()
end = time.time()
training_time = end - start
print "MIMIC Inverse of Distance: " + str(ef.value(mimic.getOptimal()))
OUTFILE = "%s%s.csv" % (OUTFILE_BASE, "MIMIC")
with open(OUTFILE, 'a+') as f:
f.write("%d,%f,%f\n" % (N, training_time, ef.value(mimic.getOptimal())))
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)
results = []
for _ in range(runs):
ga_pop = N * 5
ga_keep = int(ga_pop * .75)
ga_mut = int(ga_pop * .2)
ga = StandardGeneticAlgorithm(ga_pop, ga_keep, ga_mut, gap)
fit = FixedIterationTrainer(ga, 150)
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))
t0 = time.time()
calls = []
results = []
for _ in range(runs):
mimic = MIMIC(N / 2, N / 5, 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))
print "MIMIC, average feval calls , " + str(sum(calls) / float(runs))
t1 = time.time() - t0
print "MIMIC, average time , " + str(t1 / float(runs))
def knapsackfunc(NUM_ITEMS, iterations):
rhcMult = 600
saMult = 600
gaMult = 4
mimicMult = 3
# 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()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
optimalOut = []
timeOut = []
evalsOut = []
for niter in iterations:
iterOptimalOut = [NUM_ITEMS, niter]
iterTimeOut = [NUM_ITEMS, niter]
iterEvals = [NUM_ITEMS, 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(100, .95, 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, 150, 25, 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, 100, 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]
start = time.time()
ga = StandardGeneticAlgorithm(200, 150, 25, gap)
fit = FixedIterationTrainer(ga, iteration)
fit.train()
end = time.time()
ga_time = end - start
ga_fit = ef.value(ga.getOptimal())
# print "GA: " + str(ga_fit)
start = time.time()
mimic = MIMIC(200, 100, pop)
fit = FixedIterationTrainer(mimic, iteration / 200)
fit.train()
end = time.time()
mimic_time = end - start
mimic_fit = ef.value(mimic.getOptimal())
# print "MIMIC: " + str(mimic_fit)
row = [
iteration, rhc_fit, rhc_time, sa_fit, sa_time, ga_fit, ga_time,
mimic_fit, mimic_time
]
iterdata.append(row)
round_end = time.time()
with open("knapsack_{}.csv".format(str(n)), 'wb') as resultFile:
wr = csv.writer(resultFile, dialect='excel')
wr.writerows(iterdata)
round_end = time.time()
print "Takes total " + str(round_end - round_start) + " secs"
print(str(ef.value(sa.getOptimal())))
end = time.time()
times += "\n%0.03f" % (end - start)
print(times)
times = ""
print "GA:"
for x in range(20):
start = time.time()
iterations = (x + 1) * 250
ga = StandardGeneticAlgorithm(20, 20, 0, gap)
fit = FixedIterationTrainer(ga, iterations)
fit.train()
print(str(ef.value(ga.getOptimal())))
end = time.time()
times += "\n%0.03f" % (end - start)
print(times)
times = ""
print "MIMIC:"
for x in range(20):
start = time.time()
iterations = (x + 1) * 250
mimic = MIMIC(50, 10, pop)
fit = FixedIterationTrainer(mimic, iterations)
fit.train()
print(str(ef.value(mimic.getOptimal())))
end = time.time()
times += "\n%0.03f" % (end - start)
print(times)
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()
sa_f.close()
ga_f.close()
mimic_f.close()
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 = array('i', fill)
odd = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
mimic = MIMIC(500, 100, pop)
fit = FixedIterationTrainer(mimic, 1000)
fit.train()
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
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)
with open(OUTPUT_FILE, "w") as outFile:
for i in range(1):
outFile.write(','.join([
"MIMIC_SAMPLES", "overall_mimic_fitness",
"overall_mimic_training_time"
]) + '\n')
for i in range(len(MIMIC_SAMPLES_pool)):
outFile.write(','.join([
str(MIMIC_SAMPLES_pool[i]),
str(overall_mimic_fitness[i]),
str(overall_mimic_training_time[i])
# print(ef.boardPositions())
print("============================")
sa = SimulatedAnnealing(1E1, .1, hcp)
fit = FixedIterationTrainer(sa, 200000)
fit.train()
sa_opt = ef.value(sa.getOptimal())
print "SA: " + str(sa_opt)
# print("SA: Board Position: ")
# print(ef.boardPositions())
print("============================")
ga = StandardGeneticAlgorithm(200, 0, 10, gap)
fit = FixedIterationTrainer(ga, 1000)
fit.train()
ga_opt = ef.value(ga.getOptimal())
print "GA: " + str(ga_opt)
# print("GA: Board Position: ")
# print(ef.boardPositions())
print("============================")
mimic = MIMIC(200, 10, pop)
fit = FixedIterationTrainer(mimic, 1000)
fit.train()
mimic_opt = ef.value(mimic.getOptimal())
print "MIMIC: " + str(mimic_opt)
# print("MIMIC: Board Position: ")
# print(ef.boardPositions())
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 20000)
fit.train()
print("RHC: %0.03f" % ef.value(rhc.getOptimal()))
print(ef.foundConflict())
print("============================")
sa = SimulatedAnnealing(1E12, .1, hcp)
fit = FixedIterationTrainer(sa, 20000)
fit.train()
print("SA: %0.03f" % ef.value(sa.getOptimal()))
print(ef.foundConflict())
print("============================")
ga = StandardGeneticAlgorithm(200, 10, 60, gap)
fit = FixedIterationTrainer(ga, 50)
fit.train()
print("GA: %0.03f" % ef.value(ga.getOptimal()))
print(ef.foundConflict())
print("============================")
mimic = MIMIC(200, 100, pop)
fit = FixedIterationTrainer(mimic, 5)
fit.train()
print("MIMIC: %0.03f" % ef.value(mimic.getOptimal()))
print(ef.foundConflict())
# Result handling
last_train_time_rhc = end_rhc - start_rhc
rhc_train_time[repetition].append(last_train_time_rhc)
rhc_accuracy[repetition].append(ef.value(rhc.getOptimal()))
last_train_time_sa = end_sa - start_sa
sa_train_time[repetition].append(last_train_time_sa)
sa_accuracy[repetition].append(ef.value(sa.getOptimal()))
last_train_time_ga = end_ga - start_ga
ga_train_time[repetition].append(last_train_time_ga)
ga_accuracy[repetition].append(ef.value(ga.getOptimal()))
last_train_time_mimic = end_mimic - start_mimic
mimic_train_time[repetition].append(last_train_time_mimic)
mimic_accuracy[repetition].append(ef.value(mimic.getOptimal()))
while current_iteration_count <= MAX_ITERATION - ITERATION_STEP:
print("Computing for %d iterations" %
(current_iteration_count + ITERATION_STEP))
# Trainer declaration
fit_rhc = FixedIterationTrainer(rhc, ITERATION_STEP)
fit_sa = FixedIterationTrainer(sa, ITERATION_STEP)
fit_ga = FixedIterationTrainer(ga, ITERATION_STEP)
fit_mimic = FixedIterationTrainer(mimic, ITERATION_STEP)
# Fitting
start_rhc = time.time()
fit_rhc.train()
end_rhc = time.time()
fill = [N] * N
ranges = array('i', fill)
odd = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
mimic = MIMIC(500, 100, pop)
niters = [50, 200, 500, 800, 1000, 1200, 1500, 2000, 4000, 10000]
for iters in niters:
start = time.time()
fit = FixedIterationTrainer(mimic, iters)
# value = 0
# for isample in range(nsample):
fit.train()
#value += ef.value(mimic.getOptimal())
value = ef.value(mimic.getOptimal())
end = time.time()
clock_time = (end - start) #/nsample
value = round(value, 2)
print "MIMIC " + str(1 / value), iters, clock_time
optimal = mimic.getOptimal()
fill = [0] * optimal.size()
ddata = array('d', fill)
path = []
for i in range(0, len(ddata)):
ddata[i] = optimal.getContinuous(i)
order = ABAGAILArrays.indices(optimal.size())
ABAGAILArrays.quicksort(ddata, order)
for x in range(0, N):
path.append(order[x])
rhc0 = RandomizedHillClimbing(hcp0)
i = 0
max = 0
while (i < timeout/10):
rhc0.train()
i += 1
max = ef.value(rhc0.getOptimal())
print "rhc0,", i,",", max
goal = max
pop0 = GenericProbabilisticOptimizationProblem(ef, odd, df)
mimic0 = MIMIC(200, 100, pop)
i = 0
while ( i< timeout/1000):
mimic0.train()
i += 1
max = ef.value(mimic0.getOptimal())
print "mimic0,", i,",", max
if (max > goal):
goal = max
gap0 = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
ga0 = StandardGeneticAlgorithm(200, 100, 25, gap0)
i = 0
while ( i< timeout/1000):
ga0.train()
i += 1
max = ef.value(ga0.getOptimal())
print "ga0,", i,",", max
if (max > goal):
goal = max
# run RHC
#MIMIC
for t in range(numTrials):
for samples, keep, m in product([100, 200], [40, 80], [.4, .8]):
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')
ef = FlipFlopEvaluationFunction()
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
df = DiscreteDependencyTree(m, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
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], fevals)
print st
with open(fname, 'a') as f:
f.write(st)
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()
results = {
'num_iterations': num_iterations,
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 = array("i", fill)
odd = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(0.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
mimic = MIMIC(500, 100, pop)
fit = FixedIterationTrainer(mimic, 1000)
fit.train()
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
for iters in iters_list:
sa = SimulatedAnnealing(temp, cooling_rate, hcp)
fit = FixedIterationTrainer(sa, iters)
start = time.time()
fit.train()
duration = time.time() - start
print "Iters: " + str(
iters) + ", Temp: " + str(temp) + ", Fitness: " + str(
ef.value(sa.getOptimal())), ", Duration: " + str(duration)
print "Standard Genetic Algorithm"
for iters in iters_list:
# Population of size 20, of which 20 will mate and 0 will mutate.
ga = StandardGeneticAlgorithm(200, 200, 0, gap)
fit = FixedIterationTrainer(ga, iters)
start = time.time()
fit.train()
duration = time.time() - start
print "Iters: " + str(iters) + ", Fitness " + str(ef.value(
ga.getOptimal())) + ", Dur: " + str(duration)
print "MIMIC Algorithm"
for iters in iters_list:
mimic = MIMIC(50, 10, pop)
fit = FixedIterationTrainer(mimic, iters)
start = time.time()
fit.train()
duration = time.time() - start
print "Iters: " + str(iters) + ", Fitness: " + str(
ef.value(mimic.getOptimal())) + ", Dur: " + str(duration)
ef = ContinuousPeaksEvaluationFunction(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()))
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)
genetic_problem = GPOP(fitness, discrete_dist, discrete_dependency)
start = time.clock()
mimic_problem = MIMIC(200, 20, genetic_problem)
fit = FixedIterationTrainer(mimic_problem, iteration)
fit.train()
end = time.clock()
full_time = end - start
mimic_total += fitness.value(mimic_problem.getOptimal())
mimic_time += full_time
mimic_total_avg = mimic_total / runs
mimic_time_avg = mimic_time / runs
data = '{},{},{}\n'.format(iteration, mimic_total_avg, mimic_time_avg)
print(data)
with open(output_directory, 'a') as f:
f.write(data)
#SA Analysis
cooling_list = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95]
output_directory = "Results/Small/Continuous_Peaks_SA_Cooling.csv"
with open(output_directory, 'w') as f:
f.write('iterations,fitness,time\n')
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)
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 mimic = MIMIC(50, 10, pop) fit = FixedIterationTrainer(mimic, 50) t0 = time() fit.train() print "MIMIC: " + str(ef.value(mimic.getOptimal())), "time taken", time() - t0
