class GeneticOptimization(OptimizationStep):
"Genetic optimization step."
def __init__(self, no_vars, mins, maxes, comm, stop_flag, seed=False,
allele=False, size=200, generations=100):
super(GeneticOptimization, self).__init__(no_vars, mins, maxes, comm,
stop_flag)
self.seed = seed
self.allele = allele
self.size = size
self.timer = Timer()
self.generations = generations
self.prepare()
def __prepare_genome(self):
u"Initialize genome with constraints."
if self.allele: # Built-in allele version.
self.genome = AlleleG1DList(self.no_vars, constr_min=self.mins,
constr_max=self.maxes)
else: # Custom, ported genetic operators.
self.genome = CustomG1DList(self.no_vars)
self.genome.setParams(min_constr=self.mins, max_constr=self.maxes)
self.genome.evaluator.set(GeneticObjective(self.comm))
def __prepare_engine(self):
u"Prepare the GA engine."
# Set GA engine parameters.
self.ga = CustomGSimpleGA(self.genome, self.seed)
self.ga.setPopulationSize(self.size)
self.ga.setGenerations(self.generations)
self.ga.setParams(stop_file=self.stop_flag)
self.ga.setParams(timer=self.timer)
def prepare(self):
self.__prepare_genome()
self.__prepare_engine()
def run(self):
u"Fire the Genetic Algorithm Engine."
printf("Starting GA: %d generations of %d individuals" % (
self.generations, self.size))
self.timer.start()
self.ga.evolve()
self.__post_run()
# Return the best parameters found.
return list(self.ga.bestIndividual().getInternalList())
def __post_run(self):
stats_step_callback(self.ga) # Display final statistics.
# Evolution is stoped.
run_time = self.timer.stop()
print "GA finished in %g s." % run_time