authors argued that animals have an understanding of the whole space of all possible behaviors and that this is shaped by experience in general. Furthermore, in animals this space can be extrapolated towards novel behaviors in novel or changing contexts, e.g. when having to deal with a changed morphology. This behavioral space is evolved in a crystallized adaptivity fashion. In their view, fluid adaptivity is realized as a form of switching between behaviors in the behavioral space.

Cully et al. [@cully_robots_2015] introduced a specific, low-dimensional behavioral space that could act as a representational structure for finding stepping stones. In their case, a temporal description of the behavior of all legs was used as a lower (6-) dimensional behavior space (they used duty cycles of the individual legs). Subsequently they trained, using an EA, a behavior performance map. The goal was to cover the low dimensional behavioral space and relate each behavior description towards an optimized behavior, i.e. a high (36-) dimensional action. The optimization was done using an evolutionary approach called multi-dimensional archive of phenotypic elites (MAP-Elites [@mouret_illuminating_2015]). MAP-Elites produce a large diversity of high-performing, but qualitatively different solutions. As this approach explores more of the search space concurrently, it 