Journal
OPERATIONS RESEARCH LETTERS
Volume 39, Issue 2, Pages 150-154Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.orl.2011.01.002
Keywords
Multi-objective optimization; Estimation of distribution algorithm; Model building; Growing neural gas
Categories
Funding
- CICYT [TIN2008-06742-C02-02/TSI, TEC2008-06732-C02-02/TEC]
- SINPROB
- CAM CONTEXTS [S2009/TIC-1485, DPS2008-07029-C02-02]
- CONACyT [103570]
- [SINPROB]
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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. (c) 2011 Elsevier B.V. All rights reserved.
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