Hands-On Neuroevolution with Python

The eval_genomes function

This function evaluates the fitness of the overall population. It has the following definition:

def eval_genomes(genomes, substrate, rt_environment, params):    best_genome = None    max_fitness = 0    errors = []    for genome in genomes:        fitness, error, total_count, false_detetctions = eval_individual(                               genome, substrate, rt_environment, params)        genome.SetFitness(fitness)        errors.append(error)        if fitness > max_fitness:            max_fitness = fitness            best_genome = genome        return best_genome, max_fitness, errors

The eval_genomes function takes the list of CPPN genomes from the current population, the substrate configuration, the initialized test environment, and the ES-HyperNEAT hyperparameters as parameters.

At the beginning of the code, ...