algorithm eaGenerateUpdate

gistfile1.py

def eaGenerateUpdate(toolbox, ngen, halloffame=None, stats=None, verbose=True):
    """The CMA-ES algorithm as described in Hansen, N. (2006). *The CMA
    Evolution Strategy: A Comparing Rewiew.*
    
    :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                    operators.
    :param ngen: The number of generation.
    :param stats: A :class:`~deap.tools.Statistics` object that is updated
                  inplace, optional.
    :param halloffame: A :class:`~deap.tools.HallOfFame` object that will
                       contain the best individuals, optional.
    :param verbose: Whether or not to log the statistics.
    
    The *population* should have been generated from a strategy, while the
    toolbox should contain a reference to the update method of the chosen
    strategy.
    """
    if verbose:
        if stats is not None:
            logger = tools.EvolutionLogger(["gen", "evals"] + stats.functions.keys())
        else:
            tools.EvolutionLogger(["gen", "evals"])
        logger.logHeader()
    
    population = None
    for gen in xrange(ngen):
        population = toolbox.generate(population)
        # Evaluate the individuals
        fitnesses = toolbox.map(toolbox.evaluate, population)
        for ind, fit in zip(population, fitnesses):
            ind.fitness.values = fit
        
        if halloffame is not None:
            halloffame.update(population)
        
        # Update the Strategy with the evaluated individuals
        toolbox.update(population)
        
        if stats is not None:
            stats.update(population)
        
        if verbose:
            logger.logGeneration(evals=len(population), gen=gen, stats=stats)

    return population