def evaluate_ann(ann):
score = catandmouse.game_function(ann, True)
return score
best=None
for generation in range(generations):
for individual in population:
individual.evaluate()
population.sort(key=lambda x:x.fitness,reverse=True)
population=population[0:pop_size/2]
best=population[0]
print generation, population[0].fitness
new_population = [best]
for x in range(1,pop_size):
if random.random()<0.5:
child=random.choice(population).mutate()
new_population.append(child)
else:
p1 = random.choice(population)
p2 = random.choice(population)
child=p1.crossover(p2)
new_population.append(child)
population=new_population
from catandmouse import game_function
ann=best.create_neuron()
print game_function(ann,True)
#from pylab import *
#xlabel("Generations")
#ylabel("Avg Fitness")
#plot(fitness)
#show()
