Enhancing Divergent Search through Extinction Events

A challenge in evolutionary computation is to create repre-sentations as evolvable as those in natural evolution. This paper hypothesizes that extinction events, i.e. mass extinc-tions, can significantly increase evolvability, but only when combined with a divergent search algorithm, i.e. a search driven towards diversity (instead of optimality). Extinctions amplify diversity-generation by creating unpredictable evo-lutionary bottlenecks. Persisting through multiple such bot-tlenecks is more likely for lineages that diversify across many niches, resulting in indirect selection pressure for the capac-ity to evolve. This hypothesis is tested through experiments in two evolutionary robotics domains. The results show that combining extinction events with divergent search increases evolvability, while combining them with convergent search offers no similar benefit. The conclusion is that extinction events may provide a simple and effective mechanism to en-hance performance of divergent search algorithms