Comparing multimodal optimization and illumination

Vassilis Vassiliades 1 Konstantinos Chatzilygeroudis 1 Jean-Baptiste Mouret 1
1 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : 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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Communication dans un congrès
Genetic and Evolutionary Computation Conference (GECCO 2017), 2017, Berlin, Germany
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Dernière modification le : jeudi 11 janvier 2018 - 06:27:29
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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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