Quality-Diversity (QD) algorithms aim to discover diverse, high-performing solutions across behavioral niches. However, QD search often stagnates as incremental variation operators struggle to propagate building blocks across large populations. Existing mutation operators rely on gradual variation to solutions, limiting their ability to efficiently explore regions of the search space distant from parent solutions or to spread beneficial genetic material through the population. We propose a mutation operator which augments variation-based operators with discrete, gene-level crossover, enabling rapid recombination of elite genetic material. This crossover mechanism mirrors the biological principle of meiosis and facilitates both the direct transfer of genetic material and the exploration of novel genotype configurations beyond the existing elite hypervolume. We evaluate operators on three locomotion environments, demonstrating improvements in QD score, coverage, and max fitness, with particularly strong performance in later stages of optimization once building blocks have been established in the archive. These results show that the addition of a discrete crossover mutation provides a complementary exploration mechanism that sustains quality-diversity growth beyond the performance demonstrated by existing operators.

翻译：质量多样性（QD）算法旨在发现行为生态位中多样化且高性能的解。然而，QD搜索常因增量式变异算子难以在大型种群中传播构建模块而陷入停滞。现有变异算子依赖于对解的渐进式变异，限制了其高效探索远离父解搜索空间区域或传播有益遗传物质的能力。我们提出一种变异算子，通过离散的基因级交叉增强基于变异的算子，实现精英遗传物质的快速重组。该交叉机制模拟了生物学中的减数分裂原理，促进了遗传物质的直接转移以及对现有精英超体积之外新型基因型配置的探索。我们在三个运动环境中评估了该算子，证明了其在QD分数、覆盖率和最大适应度方面的改进，尤其在优化后期，当存档中已建立构建模块后表现出尤为优异的性能。这些结果表明，添加离散交叉变异提供了一种互补的探索机制，能够持续推动质量多样性的增长，超越现有算子所展现的性能。