import GeneticAlgorithm as GA
import Fitness
import Ordenation
import Graphic
time = 720
chromosomeSize = time/10
generations = 50
mutationPercentage = 40
populationSize = 20
numberOfSelectedIndividuals = 12
pltBestFitness = []
pltFitnessAverage = []
# Main
population = GA.initialize_population(populationSize,chromosomeSize)
for i in range(generations):
print "Generation: "+ str(i)
fitnessvalues = Fitness.fitness(population,time)
fitnessvalues,population = Ordenation.selectionsort(fitnessvalues,population)
print " Best fitness: "+str(fitnessvalues[0])
print " Fitness average: "+str(sum(fitnessvalues)/len(fitnessvalues))
pltBestFitness.append(fitnessvalues[0])
pltFitnessAverage.append(sum(fitnessvalues)/len(fitnessvalues))
population = GA.selection(population, numberOfSelectedIndividuals)
population = GA.uniformCrossover(population, populationSize, chromosomeSize)
population = GA.mutation(population, mutationPercentage)
Graphic.printGraphic(pltBestFitness,pltFitnessAverage,generations)
