RT Journal Article
T1 MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms
A1 Martí, Luis
A1 García, Jesús
A1 Berlanga de Jesús, Antonio
A1 Coello Coello, Carlos A.
A1 Molina López, José Manuel
AB We examine the model-building issue related to multi-objective estimation of distribution algorithms (MOEDAs) and show that some of their, as yet overlooked, characteristics render most current MOEDAs unviable when addressing optimization problems with many objectives. We propose a novel model-building growing neural gas (MB-GNG) network that is specially devised for properly dealing with that issue and therefore yields a better performance. Experiments are conducted in order to show from an empirical point of view the advantages of the new algorithm.
PB Elsevier
SN 0167-6377
YR 2011
FD 2011-03
LK https://hdl.handle.net/10016/18425
UL https://hdl.handle.net/10016/18425
LA eng
NO assigned to this paper for their comments and suggestions. Theyhelped to substantially improve the paper. They also wish to thankProf. Elisenda Molina for her assistance in the preparation of themanuscript. LM, JG, AB and JMM were supported by projects CICYTTIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC,SINPROB, CAM CONTEXTS S2009/TIC-1485 and DPS2008-07029-C02-02. CACC was supported by CONACyT project 103570.
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