def getObjectives(self,m,n):
obj = self.model(m,n).objectives
dec = self.model(m,n).decisions
###$print "\nRunning GA"
###$print "x = ",self.x
DifferentSeeds=random.randint(0,10000)
pf,hv=ga(self.model,decisions=dec,objectives=obj,someSeed=DifferentSeeds,candidates=self.x[2],generations=1000,mutationRate=self.x[1],lives=self.x[0])
###$print "completed GA"
self.fx=[hv]
return hv
from GeneticAlgorithm import GeneticAlgorithm as ga g1 = ga() g1.evolve(50) print(g1)
#print "start tuner..."
TunedLives,TunedMutationRate,TunedCandidates = TuneGAparametersUsingDE(model,decisions=decisions,objectives=decisions)
"Printing Tuned paramenters"
#print "Tuned:[Lives,MutationRate,Candidates]=",TunedLives,TunedMutationRate,TunedCandidates
i = 1
HV =[]
PF =[]
tunedHV=[]
tunedPF=[]
#print "Iteration...."
for k in xrange(numOfIterations):
print k,
DifferentSeeds=random.randint(0,10000)
paretoFront,HyperVolume=ga(model,decisions=decisions,objectives=objectives,someSeed=DifferentSeeds,candidates=100,generations=1000,mutationRate=0.05,lives=5)
#print " START TUNED ONE...................."
tunedPF,tunedHyperVolume=ga(model,decisions=decisions,objectives=objectives,someSeed=DifferentSeeds,candidates=TunedCandidates,generations=1000,mutationRate=TunedMutationRate,lives=TunedLives)
HV.append(HyperVolume)
PF.append(paretoFront)
tunedHV.append(tunedHyperVolume)
tunedPF.append(tunedPF)
i += 1
#print k," ITERATIONs DONE...................."
#Print data for this Decision/objective pair
print ""
print "----------------------------------------------------------------"
print "Finished processing ",model.__name__," with Decisions = ",decisions," and Objectives = ",objectives
fo.write('++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n')
