Evolvability refers to the ability of an individual genotype (solution) to produce offspring with mutually diverse phenotypes. Recent research has demonstrated that divergent search methods, particularly novelty search, promote evolvability by implicitly creating selective pressure for it. The main objective of this paper is to provide a novel perspective on the relationship between neuroevolutionary divergent search and evolvability. In order to achieve this, several types of walks from the literature on fitness landscape analysis are first adapted to this context. Subsequently, the interplay between neuroevolutionary divergent search and evolvability under varying amounts of evolutionary pressure and under different diversity metrics is investigated. To this end, experiments are performed on Fetch Pick and Place, a robotic arm task. Moreover, the performed study in particular sheds light on the structure of the genotype-phenotype mapping (the behavior landscape). Finally, a novel definition of evolvability that takes into account the evolvability of offspring and is appropriate for use with discretized behavior spaces is proposed, together with a Markov-chain-based estimation method for it.

翻译：可进化性指个体基因型（解决方案）产生具有互异表型后代的能力。最新研究表明，发散搜索方法（尤其是新颖性搜索）通过隐性创建选择压力来促进可进化性。本文旨在为神经进化发散搜索与可进化性之间的关系提供全新视角。为实现这一目标，首先将适应度景观分析文献中的多种游走方法适配至本领域，随后探究不同进化压力强度与多样性度量条件下神经进化发散搜索与可进化性的相互作用。为此，我们在Fetch Pick and Place机械臂任务上开展实验。该研究特别揭示了基因型-表型映射（行为景观）的结构特征。最后，提出一种考虑后代可进化性且适用于离散化行为空间的新型可进化性定义，并给出基于马尔可夫链的估值方法。