dimishing_ETA_BPO.py

from deap import tools
def _get_offspring_time_diminishing_eta(parents, toolbox, cxpb, mutpb,gen, ngen):
	'''return the offspring, use toolbox.variate if possible'''


	BOUND_LOW = []
	BOUND_UP = []
	NDIM = len(parents[0])
	fit_dim = len(parents[0].fitness.values)
	for x in range(0,len(parents[0])):
		BOUND_LOW.append(toolbox.uniformparams.args[0][x])
		BOUND_UP.append(toolbox.uniformparams.args[1][x])
	ETA = int(30.0*(1-(gen/ngen)))
	toolbox.register("mate", tools.cxSimulatedBinaryBounded, low=BOUND_LOW, up=BOUND_UP, eta=ETA)
	toolbox.register("mutate", tools.mutPolynomialBounded, low=BOUND_LOW, up=BOUND_UP, eta=ETA, indpb=1.0/NDIM)
	if (1-(gen/ngen))/2.0>0.05:
		mutpb = (1-(gen/ngen))/2.0
	else:
		mutpb = 0.05
	if (1-(gen/ngen))>0.4:
		cxpb = (1-(gen/ngen))/1.1
	else:
		cxpb = 0.4

	if hasattr(toolbox, 'variate'):

		return toolbox.variate(parents, toolbox, cxpb, mutpb)
	return deap.algorithms.varAnd(parents, toolbox, cxpb, mutpb)

def _get_offspring(parents, toolbox, cxpb, mutpb):
	'''return the offspring, use toolbox.variate if possible'''

	if hasattr(toolbox, 'variate'):
		return toolbox.variate(parents, toolbox, cxpb, mutpb)
	return deap.algorithms.varAnd(parents, toolbox, cxpb, mutpb)


def _check_stopping_criteria(criteria, params):
    for c in criteria:
        c.check(params)
        if c.criteria_met:
            logger.info("Run stopped because of stopping criteria: " + c.name)
            return True
    else:
        return False

def filter_parents(parents):
    """
    --synopsis: This method does not work yet.
    #Remove the genes that represent the cliff
    #edge. Possibly counter productive.
    """
    import copy
    parentsc = copy.copy(parents)
    orig_len = len(parents)
    cnt = 0
    for p in parentsc:
        flag=False
        for fv in p.fitness.values:
            if fv == 1000.0:
                flag=True
        if flag:
            cnt += 1
    while cnt > 0:
        for i, p in enumerate(parentsc):
            flag=False
            for fv in p.fitness.values:
                if fv == 1000.0:
                    flag=True
            if flag:
                del parentsc[i]
                cnt -= 1
    cnt=0
    while len(parentsc) < orig_len:
        parentsc.append(parentsc[cnt])
        cnt+=1
    return parentsc