Lamarckian inheritance has been shown to be a powerful accelerator in systems where the joint evolution of robot morphologies and controllers is enhanced with individual learning. Its defining advantage lies in the offspring inheriting controllers learned by their parents. The efficacy of this option, however, relies on morphological similarity between parent and offspring. In this study, we examine how Lamarckian inheritance performs when the search process is driven toward high morphological variance, potentially straining the requirement for parent-offspring similarity. Using a system of modular robots that can evolve and learn to solve a locomotion task, we compare Darwinian and Lamarckian evolution to determine how they respond to shifting from pure task-based selection to a multi-objective pressure that also rewards morphological novelty. Our results confirm that Lamarckian evolution outperforms Darwinian evolution when optimizing task-performance alone. However, introducing selection pressure for morphological diversity causes a substantial performance drop, which is much greater in the Lamarckian system. Further analyses show that promoting diversity reduces parent-offspring similarity, which in turn reduces the benefits of inheriting controllers learned by parents. These results reveal the limits of Lamarckian evolution by exposing a fundamental trade-off between inheritance-based exploitation and diversity-driven exploration.

翻译：拉马克式遗传已被证明能够在机器人形态与控制器的联合进化中通过个体学习显著加速进化过程。其核心优势在于后代能够继承父母习得的控制器。然而，这一机制的有效性取决于亲代与子代之间的形态相似性。本研究考察了当搜索过程向高形态方差方向推进时（这可能会破坏亲代-子代相似性的要求），拉马克式遗传的表现。通过构建能够进化并学习解决运动任务的模块化机器人系统，我们比较了达尔文式与拉马克式进化在从纯任务导向选择转向同时奖励形态新颖性的多目标压力时的响应差异。结果表明：在仅优化任务性能时，拉马克式进化确实优于达尔文式进化；但当引入形态多样性的选择压力后，系统性能出现显著下降，且拉马克式系统的下降幅度远大于达尔文式系统。进一步分析显示，促进多样性会降低亲代-子代相似性，进而削弱继承父母习得控制器的收益。这些结果揭示了拉马克式进化的局限性——遗传驱动的开发性与多样性驱动的探索性之间存在根本性权衡。