Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objective

However, it was widely known that successfully converging to the maximum of that fitness function requires maintaining genetic diversity in the population of solutions (e.g., [1-4]). Moreover, the use of niching demonstrated how maintaining subpopulations could help find multiple solutions to a single problem [5]. Some studies included genetic diversity as one of the objectives of the EA [6]. Approaches in evolutionary robotics, artificial life, and neuro-evolution realised that genetic diversity does not necessarily imply a diversity of solutions, since (i) different genotypes may encode the same behaviour and vice versa; and (ii) many genotypes may encode unsafe or undesirable solutions that should be discarded during evolution (e.g., when a robot crashes into an obstacle). Such approaches began to emphasise behavioural diversity [7-10], not only as a driver for objective-based evolution but also as the enabler for diversity-or novelty-driven evolution [11]. In quality-diversity (QD) algorithms such as MAP-Elites [12] and Novelty Search with Local Competition [13], the behavioural diversity approach is combined with local competition such that the best solution for each local region in the behaviour space is stored, forming a large archive of high-quality solutions. The development of quality-diversity algorithms has allowed a plethora of applications.