linear_ranking_selection.py 文件源码

def select(self, population, fitness):
        '''
        Select a pair of parent individuals using linear ranking method.
        '''
        # Individual number.
        NP = len(population)

        # Add rank to all individuals in population.
        all_fits = population.all_fits(fitness)
        indvs = population.individuals
        sorted_indvs = sorted(indvs,
                              key=lambda indv: all_fits[indvs.index(indv)])

        # Assign selection probabilities linearly.
        # NOTE: Here the rank i belongs to {1, ..., N}
        p = lambda i: (self.pmin + (self.pmax - self.pmin)*(i-1)/(NP-1))
        probabilities = [self.pmin] + [p(i) for i in range(2, NP)] + [self.pmax]

        # Normalize probabilities.
        psum = sum(probabilities)
        wheel = list(accumulate([p/psum for p in probabilities]))

        # Select parents.
        father_idx = bisect_right(wheel, random())
        father = sorted_indvs[father_idx]
        mother_idx = (father_idx + 1) % len(wheel)
        mother = sorted_indvs[mother_idx]

        return father, mother