Hypervolume-Based Local Search in Multi-Objective Evolutionary Optimization

0430976 - ÚI 2015 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Pilát, M. - Neruda, Roman
Hypervolume-Based Local Search in Multi-Objective Evolutionary Optimization.
GECCO '14. Proceedings of the 2014 Conference on Genetic and Evolutionary Computation. New York: ACM, 2014 - (Igel, C.; Arnold, D.), s. 637-644. ISBN 978-1-4503-2662-9.
[GECCO 2014. Genetic and Evolutionary Computation Conference. Vancouver (CA), 12.07.2014-16.07.2014]
Grant CEP: GA MŠMT(CZ) LD13002
Institucionální podpora: RVO:67985807
Klíčová slova: multi-objective optimization * surrogate modeling * NSGA-II * hyper-volume
Kód oboru RIV: IN - Informatika

This paper describes a surrogate based multi-objective evolutionary algorithm with hyper-volume contribution-based local search. The algorithm switches between an NSGA-II phase and a local search phase. In the local search phase, a model for each of the objectives is trained and CMAES is used to optimize the hyper-volume contribution of each individual with respect to its two neighbors on the non-dominated front. The performance of the algorithm is evaluated using the well known ZDT and WFG benchmark suites.
Trvalý link: http://hdl.handle.net/11104/0235641