Konstantinos I. Chatzilygeroudis

Comparing multimodal optimization and illumination

Illumination algorithms are a recent addition to the evolutionary computation toolbox that allows the generation of many diverse and high-performing solutions in a single run. Nevertheless, traditional multimodal optimization algorithms also search for diverse and high-performing solutions: could some multimodal optimization algorithms be better at illumination than illumination algorithms? In this study, we compare two illumination algorithms (Novelty Search with Local Competition (NSLC), MAP-Elites) with two multimodal optimization ones (Clearing, Restricted Tournament Selection) in a maze navigation task. The results show that Clearing can have comparable performance to MAP-Elites and NSLC.