Martí, LuisGarcía, JesúsBerlanga de Jesús, AntonioMolina López, José Manuel2014-03-282014-03-282010-07GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation. (2010) ACM, New York, USA, 545-546978-1-4503-0072-8https://hdl.handle.net/10016/18667Proceedings of: 12th annual conference on Genetic and evolutionary computation (GECCO '10). Portland, Oregon, USA, July 7-11, 2010.In this work we analyze the model-building issue and the requirements it imposes on the learning paradigm being used. We argue that error-based learning, the class of learning most commonly used in MOEDAs, is responsible for current MOEDA underachievement. We present ART as a viable alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume based selector as described for the HypE algorithm. We experimentally show that thanks to MARTEDA's novel model-building approach and an indicator-based population ranking the algorithm it is able to outperform similar MOEDAs and MOEAs.application/pdfeng© ACM, 2010Multi-objective OptimizationEstimation of Distribution AlgorithmsAdaptive Resonance Theoryconference paperInformática10.1145/1830483.1830585open access545546GECCO 2010 : Genetic and Evolutionary Computation Conference : Wednesday-Sunday, July 7-11, 2010, Portland, OregonCC/0000011615