Martí, LuisGarcía, JesúsBerlanga de Jesús, AntonioMolina, José M.2014-07-032014-08-012013-08Annals of Mathematics and Artificial Intelligence (2013), 68 (4), 247-273.1012-2443 (print)1573-7470 (online)https://hdl.handle.net/10016/19048The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.27application/pdfeng© 2012 Springer Science+Business Media B.VMulti-objective optimizationEstimation of Distribution AlgorithmsAdaptive Resonance theoryMulti-objective OptimizationEvolutionary AlgorithmNeural NetworkHypervolumeModelresearch articleInformática10.1007/s10472-012-9303-0open access2474273Annals of Mathematics and Artificial Intelligence68AR/0000014564