def _next_generation(self, sorted_trials):
        """Generate genes (encodings) for the next generation.

        Use the top K (_keep_top_ratio) trials of the last generation
        as candidates to generate the next generation. The action could
        be selection, crossover and mutation according corresponding
        ratio (_selection_bound, _crossover_bound).

        Args:
            sorted_trials: List of finished trials with top
                performance ones first.

        Returns:
            A list of new genes (encodings)
        """

        candidate = []
        next_generation = []
        num_population = self._next_population_size(len(sorted_trials))
        top_num = int(max(num_population * self._keep_top_ratio, 2))

        for i in range(top_num):
            candidate.append(sorted_trials[i].extra_arg)
            next_generation.append(sorted_trials[i].extra_arg)

        for i in range(top_num, num_population):
            flip_coin = np.random.uniform()
            if flip_coin < self._selection_bound:
                next_generation.append(GeneticSearch._selection(candidate))
            else:
                if flip_coin < self._selection_bound + self._crossover_bound:
                    next_generation.append(GeneticSearch._crossover(candidate))
                else:
                    next_generation.append(GeneticSearch._mutation(candidate))
        return next_generation
Generate genes (encodings) for the next generation.

        Use the top K (_keep_top_ratio) trials of the last generation
        as candidates to generate the next generation. The action could
        be selection, crossover and mutation according corresponding
        ratio (_selection_bound, _crossover_bound).

        Args:
            sorted_trials: List of finished trials with top
                performance ones first.

        Returns:
            A list of new genes (encodings)