CMA-ES generate update example

gistfile1.py

import numpy

from deap import algorithms
from deap import base
from deap import benchmarks
from deap import cma
from deap import creator
from deap import tools

# Problem size
N=30

creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin)

def main():
    # The cma module uses the numpy random number generator
    numpy.random.seed(128)

    # The CMA-ES algorithm takes a population of one individual as argument
    # The centroid is set to a vector of 5.0 see http://www.lri.fr/~hansen/cmaes_inmatlab.html
    # for more details about the rastrigin and other tests for CMA-ES    
    strategy = cma.Strategy(centroid=[5.0]*N, sigma=5.0, lambda_=20*N)
    strategy.register("evaluate", benchmarks.rastrigin)
    strategy.register("individual", creator.Individual)

    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", tools.mean)
    stats.register("std", tools.std)
    stats.register("min", min)
    stats.register("max", max)
    #logger = tools.EvolutionLogger(stats.functions.keys())
   
    # The CMA-ES algorithm converge with good probability with those settings
    algorithms.eaGenerateUpdate(strategy, ngen=250, stats=stats)
    
    # print "Best individual is %s, %s" % (hof[0], hof[0].fitness.values)
    # return hof[0].fitness.values[0]

if __name__ == "__main__":
    main()