class EA:
def __init__(self ,numOfGenerations, numOfChildren, numOfAdults, numOfParents, bitsInGenotypes, fitnessMethod, adultSelectionMethod, parentSelectionMethod):
self.numOfGenerations = numOfGenerations
self.numOfChildren = numOfChildren
self.numOfAdults = numOfAdults
self.numOfParents = numOfParents
self.pop = Population(bitsInGenotypes)
self.adultSelectionMethod = adultSelectionMethod
self.parentSelectionMethod = parentSelectionMethod
self.pop.initializeChildPopulation(self.numOfChildren)
self.startEvolution()
def startEvolution(self):
for generation in range(self.numOfGenerations):
self.pop.developGenotypePopulation()
self.pop.testPopulation()
fitnessSum = 0
for c in self.pop.developedChildren:
fitnessSum += c.testScore
print(generation)
print(fitnessSum)
self.adultSelection()
self.parentSelection()
self.pop.mateSelectedParents(self.numOfChildren)
def adultSelection(self):
if self.adultSelectionMethod == 0:
self.pop.fullGenerationalReplacement()
if self.adultSelectionMethod == 1:
self.pop.overProduction(self.numOfAdults)
if self.adultSelectionMethod == 2:
self.pop.generationalMixing(self.numOfAdults)
def parentSelection(self):
if self.parentSelectionMethod == 0:
self.pop.fitnessProportonateParentSelection(self.numOfParents)
if self.parentSelectionMethod == 1:
self.pop.sigmaScalingParentSelection(self.numOfParents)
if self.parentSelectionMethod == 2:
self.pop.tournamentParentSelection(self.numOfParents)
if self.parentSelectionMethod == 3:
self.pop.boltzmannParentSelection(self.numOfParents)
