Evolutionary Robotics offers the possibility to design robots to solve a specific task automatically by optimizing their morphology and control together. However, this co-optimization of body and control is challenging, because controllers need some time to adapt to the evolving morphology - which may make it difficult for new and promising designs to enter the evolving population. A solution to this is to add intra-life learning, defined as an additional controller optimization loop, to each individual in the evolving population. A related problem is the lack of diversity often seen in evolving populations as evolution narrows the search down to a few promising designs too quickly. This problem can be mitigated by implementing full generational replacement, where offspring robots replace the whole population. This solution for increasing diversity usually comes at the cost of lower performance compared to using elitism. In this work, we show that combining such generational replacement with intra-life learning can increase diversity while retaining performance. We also highlight the importance of performance metrics when studying learning in morphologically evolving robots, showing that evaluating according to function evaluations versus according to generations of evolution can give different conclusions.

翻译：进化机器人学通过同时优化机器人的形态与控制，为实现针对特定任务的自动化机器人设计提供了可能。然而，这种躯体与控制的协同优化具有挑战性，因为控制器需要一定时间来适应不断演化的形态——这可能导致新颖且有潜力的设计难以进入演化群体。一种解决方案是在演化群体中的每个个体内部添加生命周期内学习，即增加一个额外的控制器优化循环。另一个相关问题是演化群体中常常缺乏多样性，因为进化过程会过快地将搜索范围缩小到少数有前景的设计。通过实施完全代际替换——即子代机器人完全取代整个群体——可以缓解这一问题。然而，这种提升多样性的方案通常以牺牲性能为代价，相较于采用精英保留策略。本研究表明，将此类代际替换与生命周期内学习相结合，能够在保持性能的同时增加多样性。此外，我们强调了在形态演化机器人中研究学习时性能评估指标的重要性：基于函数评估次数与基于进化代数进行评估可能得出不同的结论。