Analyzing deception, evolvability, and behavioral rarity in evolutionary robotics

A common aim across evolutionary search is to skillfully navigate complex search spaces, which requires search algorithms that exploit search space structure. This paper focuses on evolutionary robotics (ER) in particular, wherein controllers for robots are evolved to produce complex behavior. One productive approach for probing search space structure is to analyze properties of fitness landscapes; however, this paper argues that ER may require a fresh perspective for landscape analysis, because ER often goes beyond the black-box setting, i.e. evaluations provide useful information about how robots behave, beyond scalar performance heuristics. Indeed, some ER algorithms explicitly exploit such behavioral information, e.g. to follow gradients of behavioral novelty rather than to climb gradients of increasing performance. Thus well-motivated behavior-aware metrics may aid probing search-space structure in ER. In particular, this paper argues that behavioral conceptions of deception, evolvability, and rarity may help to understand ER landscapes, and seeks to quantify and explore them within a common ER benchmark task. To help this investigation, an expressive but limited encoding is designed, such that the behavior of all possible individuals in the domain can be precomputed. The result is an efficient platform for experimentation that facilitates (1) probing exact quantifications of deception, evolvability, and rarity in the chosen domain, and (2) the ability to efficiently drive search through idealistic ground-truth measures. The results help develop intuitions and suggest possible new ER algorithms. The hope is that the extensible open-source framework enables quick experimentation and idea generation, aiding brainstorming of new search algorithms and measures.