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Communication Dans Un Congrès Année : 2017

Comparing multimodal optimization and illumination

Résumé

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 beeer 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. e results show that Clearing can have comparable performance to MAP-Elites and NSLC.
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Dates et versions

hal-01518802 , version 1 (05-05-2017)

Identifiants

  • HAL Id : hal-01518802 , version 1

Citer

Vassilis Vassiliades, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret. Comparing multimodal optimization and illumination. Genetic and Evolutionary Computation Conference (GECCO 2017), 2017, Berlin, Germany. ⟨hal-01518802⟩
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