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Evolvability refers to the ability of an individual genotype (solution) toproduce offspring with mutually diverse phenotypes. Recent research hasdemonstrated that divergent search methods, particularly novelty search,promote evolvability by implicitly creating selective pressure for it. The mainobjective of this paper is to provide a novel perspective on the relationshipbetween neuroevolutionary divergent search and evolvability. In order toachieve this, several types of walks from the literature on fitness landscapeanalysis are first adapted to this context. Subsequently, the interplay betweenneuroevolutionary divergent search and evolvability under varying amounts ofevolutionary pressure and under different diversity metrics is investigated. Tothis end, experiments are performed on Fetch Pick and Place, a robotic armtask. Moreover, the performed study in particular sheds light on the structureof the genotype-phenotype mapping (the behavior landscape). Finally, a noveldefinition of evolvability that takes into account the evolvability ofoffspring and is appropriate for use with discretized behavior spaces isproposed, together with a Markov-chain-based estimation method for it.

On Evolvability and Behavior Landscapes in Neuroevolutionary Divergent Search