plt.show()
def sample_fitness(genome):
sum_weight = sum(
[conn.weight for conn in genome.connection_genes.values()])
if sum_weight > 50:
return 1.0
return 0.0
if __name__ == "__main__":
i = Innovator()
r = Random()
e = Evaluator(sample_fitness, i, [TestNode])
iters = 100
e.initialize_population(size=(1, 2))
for _ in progressbar.progressbar(range(iters)):
e.evolve_generation()
pop = list(e.previous_generation.items())
pop.sort(key=lambda x: x[1].fitness, reverse=True)
for (genome_id, genome) in pop[:1]:
genome.vizualize_genome(
genome_id, "genome {} -- {}".format(
genome_id, 1 / genome.fitness if genome.fitness > 0 else 0))
plt.show()
