def iteration(self, i):
parents = range(self.__nrIndividuals)
#two parent => one child
nrChildren = len(parents) // 2
#generate the population of offsprings
offspringPopulation = Population(nrChildren)
#create the offsprings
for i in range(nrChildren):
#random indexes for the parents
crossover = False
while not crossover:
i1 = random.randint(0, self.__nrIndividuals - 1)
i2 = random.randint(0, self.__nrIndividuals - 1)
#select parents
if (i1 != i2):
crossover = True
#combine parents
parent1 = self.population.individuals[i1]
parent2 = self.population.individuals[i2]
offspringPopulation.individuals[i] = Chromosome.crossover(
parent1, parent2, self.probability_crossover)
#mutate offspring
offspringPopulation.individuals[i].mutate(
self.probability_mutate)
#evaluating the offspring population
offspringPopulation.evaluate(self.inputTrain, self.outputTrain)
#we add the population of children to the population of parents
self.population.reunion(offspringPopulation)
#select tot nrIndividuals from the new population, i.e. only the best individuals survive to the next generation
self.population.selection(self.__nrIndividuals)
def test_randSinglePoint(self):
parents = (Chromosome([1, 1, 1, 1, 1, 1]), Chromosome([0, 0, 0, 0, 0, 0]))
results = (Chromosome([1, 1, 1, 1, 0, 0]), Chromosome([0, 0, 0, 0, 1, 1]))
children = Chromosome.crossover(parents, seed=5)
self.assertEqual(len(parents[0]), len(children[0]), "Children are not same length")
self.assertEqual(len(parents[1]), len(children[1]), "Children are not same length")
self.assertEqual(results, children, "Incorrect result")
def iteration(self, i):
parents = range(self.nrInd)
nrChilds = len(parents) // 2
offspring = Population(nrChilds, self.depht // 2)
for i in range(nrChilds):
ch = Chromosome(self.depht)
offspring.arr[i] = ch.crossover(self.pop.arr[i << 1],
self.pop.arr[(i << 1) | 1])
offspring.arr[i].mutate(self.probability_mutate)
offspring.fitness_eval(self.xTrain, self.yTrain)
self.pop.reunion(offspring)
self.pop.selection(self.nrInd)
def iteration(self, i):
parents = range(self.nrInd)
nrChildren = len(parents) // 2
offspring = Population(nrChildren)
for i in range(nrChildren):
offspring.individuals[i] = Chromosome.crossover(
self.population.individuals[i << 1],
self.population.individuals[(i << 1) | 1],
self.probability_crossover)
offspring.individuals[i].mutate(self.probability_mutate)
offspring.evaluate(self.inputTrain, self.outputTrain)
self.population.reunion(offspring)
self.population.selection(self.nrInd)
def iteration(self, i):
offspring = Population(self.nrOfIndividuals)
for child in range(self.nrOfIndividuals):
first_pick = random.randint(0, self.nrOfIndividuals)
second_pick = first_pick
while second_pick == first_pick:
second_pick = random.randint(0, self.nrOfIndividuals)
offspring.individuals[child] = Chromosome.crossover(
self.population.individuals[first_pick],
self.population.individuals[second_pick],
self.crossoverProbability)
offspring.individuals[child].mutate(self.mutationProbability)
offspring.evaluate(self.inputTraining, self.outputTraining)
self.population.reunion(offspring)
self.population.selection(self.nrOfIndividuals)
print "Best Four Chromosomes in Gen",i+1
for i in range(4):
index = Chromosome.getFittestChromosome(initialPopulation)
nextPopulation.append(initialPopulation[index])
print initialPopulation[index].rankString
initialPopulation.pop(index)
print "Undergoing Crossover and Mutation"
for i in range(totalPopulation/2 - 2):
firstParentIndex = random.randrange(0,len(initialPopulation),1)
firstParent = initialPopulation[firstParentIndex]
initialPopulation.pop(firstParentIndex)
secondParentIndex = random.randrange(0,len(initialPopulation),1)
secondParent = initialPopulation[secondParentIndex]
initialPopulation.pop(secondParentIndex)
result = Chromosome.crossover(firstParent,secondParent)
result[0].mutate(maxRank)
result[1].mutate(maxRank)
nextPopulation = nextPopulation + result
initialPopulation = nextPopulation
finalPopulation = Chromosome.calculateProbability(initialPopulation,userChromosome)
print "Final Population"
Chromosome.printPopulation(finalPopulation)
print "--------"
print "Best Chromosome : ", finalPopulation[Chromosome.getFittestChromosome(finalPopulation)].rankString
print "Best Four Chromosomes in Gen",i+1
for i in range(4):
index = Chromosome.getFittestChromosome(initialPopulation)
nextPopulation.append(initialPopulation[index])
print initialPopulation[index].rankString
initialPopulation.pop(index)
print "Undergoing Crossover and Mutation"
for i in range(totalPopulation/2 - 2):
firstParentIndex = random.randrange(0,len(initialPopulation),1)
firstParent = initialPopulation[firstParentIndex]
initialPopulation.pop(firstParentIndex)
secondParentIndex = random.randrange(0,len(initialPopulation),1)
secondParent = initialPopulation[secondParentIndex]
initialPopulation.pop(secondParentIndex)
result = Chromosome.crossover(firstParent,secondParent)
result[0].mutate(maxRank)
result[1].mutate(maxRank)
nextPopulation = nextPopulation + result
initialPopulation = nextPopulation
finalPopulation = Chromosome.calculateProbability(initialPopulation,userChromosome)
print "Final Population"
Chromosome.printPopulation(finalPopulation)
print "--------"
print "Best Chromosome : ", finalPopulation[Chromosome.getFittestChromosome(finalPopulation)].rankString
