def ga(maxPop, mutation_rate, chromosome_length, maxGen):
pop = Population(maxPop,
mutation_rate,
chromosome_length=chromosome_length)
# populating with maxPop elements
pop.populate()
i = 0
for i in range(maxGen):
# calculating fitness
pop.calc_fitness()
# natural selection
parents = pop.selection()
# crossover + mutation
pop.crossover(parents)
pop.mutate()
# printing
print('Gen ' + str(i) + ": ", pop.getFittest()[0], pop.getAvgFitness())
pop.population[pop.getFittest()[1]].plot()
pop.population[pop.getFittest()[1]].plot()
initial_fitess_values = map(population.fitness_function, population)
#print initial_fitess_values
number_generations = 100
population.sus_sampler(initial_fitess_values)
max_list = []
mean_list = []
for i in xrange(number_generations):
#print "initial population"
#for i in population:
# print i.rep
#print "before crossover"
population.crossover()
#print "after crossover"
#for i in population:
# print i.rep
#print "before mutation"
population.mutation()
#for i in population:
# print i.rep
#print "after mutation"
fitness_vals = map(population.fitness_function, population)
population.sus_sampler(fitness_vals)
mean_val = np.mean(fitness_vals)
max_val = max(fitness_vals)
print str(mean_val) + "," + str(max_val)
mean_list.append(mean_val)
max_list.append(max_val)
# print i.transform_individual(signal_ranges)
initial_fitess_values = map(population.fitness_function, population)
#print initial_fitess_values
number_generations = 100
population.sus_sampler(initial_fitess_values)
max_list = []
mean_list = []
for i in xrange(number_generations):
#print "initial population"
#for i in population:
# print i.rep
#print "before crossover"
population.crossover()
#print "after crossover"
#for i in population:
# print i.rep
#print "before mutation"
population.mutation()
#for i in population:
# print i.rep
#print "after mutation"
fitness_vals = map(population.fitness_function, population)
population.sus_sampler(fitness_vals)
mean_val = np.mean(fitness_vals)
max_val = max(fitness_vals)
print str(mean_val) + "," + str(max_val)
mean_list.append(mean_val)
max_list.append(max_val)