Recombination for learning strategy parameters in the MO-CMA-ES

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a variable-metric algorithm for real-valued vector optimization. It maintains a parent population of candidate solutions, which are varied by additive, zero-mean Gaussian mutations. Each individual learns its own covariance matrix for the mutation distribution considering only its parent and offspring. However, the optimal mutation distribution of individuals that are close in decision space are likely to be similar if we presume some notion of continuity of the optimization problem. Therefore, we propose a lateral (inter-individual) transfer of information in the MO-CMA-ES considering also successful mutations of neighboring individuals for the covariance matrix adaptation. We evaluate this idea on common bi-criteria objective functions. The preliminary results show that the new adaptation rule significantly improves the performance of the MO-CMA-ES.

TitelEvolutionary Multi-Criterion Optimization : 5th International Conference, EMO 2009, Nantes, France, April 7-10, 2009. Proceedings
RedaktørerMatthias Ehrgott, Carlos M. Fonseca, Xavier Gandibleux, Jin-Kao Hao, Marc Sevaux
Antal sider14
ISBN (Trykt)978-3-642-01019-4
ISBN (Elektronisk)978-3-642-01020-0
StatusUdgivet - 2009
Eksternt udgivetJa
Begivenhed5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009 - Nantes, Frankrig
Varighed: 7 apr. 200910 apr. 2009


Konference5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009
SponsorUniversité de Nantes, Université de Bretagne Sud, Université d'Angers, École centrale de Nantes, Ecole des Mines de Nantes
NavnLecture notes in computer science

ID: 168462009