  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.
