MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms

  • Martí, Luis
  • García, Jesús
  • Berlanga de Jesús, Antonio
  • Coello Coello, Carlos A.
  • Molina López, José Manuel
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., assigned to this paper for their comments and suggestions. They helped to substantially improve the paper. They also wish to thank Prof. Elisenda Molina for her assistance in the preparation of the manuscript. LM, JG, AB and JMM were supported by projects CICYT TIN2008-06742-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., Publicado