def revolution(self, args):
#config
if (args.crossmode == "one"):
cross_func = Crossover.onePoint
elif (args.crossmode == "two"):
cross_func = Crossover.twoPoints
else:
cross_func = Crossover.randomPoints
# Prepare dataset
dataset = readDataset(args.dpath)
# Create individuals & population
ppl = Population()
for i in range(args.individuals):
individual = Individual()
individual.createGene(dataset, args.gene)
ppl.addInd(individual)
# Evolution
for i in range(args.revolution):
# Evaluation population in individuals
ppl.calcFitness(self.evaluate)
if ((i % 10) == 0):
ppl.show()
# Select parents
if (args.elite):
parents = Select.Elite(ppl, args.esize, args.mode)
parents.extend(
Select.Tournament(ppl, args.individuals - args.esize,
args.tornsize, args.mode))
else:
parents = Select.Tournament(ppl, args.individuals,
args.tornsize, args.mode)
# Clossover
children = cross_func(parents, args.individuals, args.gene,
args.crossrate)
# Mutation
children = Mutation.mutation(children, args.mutaterate, dataset)
# Swap children
ppl.setPpl(children)
# show result
ppl.calcFitness(self.evaluate)
ppl.show()
gene1[j] = gene2[j]
child.setGene(gene1)
children.append(child)
return (children)
if __name__ == "__main__":
from readDataset import readDataset
from individual import Individual
from select import Select
dataset = readDataset("./dataset/binary.txt")
population = Population()
for i in range(5):
ind = Individual()
ind.createGene(dataset, 10)
population.addInd(ind)
def evaluate(ind):
fitness = sum(ind)
return (fitness)
population.calcFitness(evaluate)
population.show()
parents = Select.Tournament(population, 5, 3, "max")
for ind in parents:
ind.show()
print("Onepoint")
children = Crossover.onePoint(parents, 5, 10, 0.7)
for ind in children:
ind.show()
print("Twopoints")
