def _internal_variation(self, random, candidates, args):
        num_offspring = args.setdefault('num_offspring', 1)
        bounder = args['_ec'].bounder        
        num_genes = max([len(x) for x in candidates])
        genes = [[x[i] for x in candidates] for i in range(num_genes)] 
        mean = [float(sum(x)) / float(len(x)) for x in genes]
        stdev = [sum([(x - m)**2 for x in g]) / float(len(g) - 1) for g, m in zip(genes, mean)]
        offspring = []
        for _ in range(num_offspring):
            child = copy.copy(candidates[0])
            for i, (m, s) in enumerate(zip(mean, stdev)):
                child[i] = m + random.gauss(0, s)
            child = bounder(child, args)
            offspring.append(child)
        return offspring
dataset/ETHPy150Open aarongarrett/inspyred/inspyred/ec/ec.py/EDA._internal_variation/original