class SimpleDemoGA:
def __init__(self, numberOfIndividuals, numberOfGenes):
self.numberOfGenes = numberOfGenes
self.population = Population(numberOfIndividuals, numberOfGenes)
self.generationCount = 0
def crossover(self):
# Swap values up to crossoverIndex between fittest and secondFittest
# This creates a child genome that has a combination of genes from the parents
# It is not a completely random selection, this function treats each bit as a gene in a sequence
crossoverIndex = random.randrange(self.numberOfGenes)
offspring = Individual(self.numberOfGenes)
tempOffspringGenes = self.population.fittest.genes[0:crossoverIndex]
for i in range(crossoverIndex, self.numberOfGenes):
tempOffspringGenes.append(self.population.secondFittest.genes[i])
# ENHANCEMENT: Add a function to Individual to make a new Individual with specific genes
# rather than create a new random one and set the genes
offspring.genes = tempOffspringGenes
return offspring
def mutation(self, offspring):
mutationIndex = random.randrange(self.numberOfGenes)
# Bitflip the mutated index using XOR
offspring.genes[mutationIndex] ^= 1
# def getFittestOffspring(self):
# return self.offspring
def addFittestOffspring(self, offspring):
self.population.removeLeastFit()
self.population.addToPopulation(offspring)
def showGeneticPool(self):
print("==Genetic Pool==")
for individual in self.population.individuals:
print("Individual: {0} Fit: {1} {2}".format(
self.population.individuals.index(individual),
individual.fitness, individual.genes))
print("================")
