RT Conference Proceedings
T1 Moving away from error-based learning in multi-objective estimation of distribution algorithms
A1 Martí, Luis
A1 García, Jesús
A1 Berlanga de Jesús, Antonio
A1 Molina López, José Manuel
AB 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.
PB ACM
SN 978-1-4503-0072-8
YR 2010
FD 2010-07
LK https://hdl.handle.net/10016/18667
UL https://hdl.handle.net/10016/18667
LA eng
NO Proceedings of: 12th annual conference on Genetic and evolutionary computation(GECCO '10). Portland, Oregon, USA, July 7-11, 2010.
NO This work was supported in part by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, DPS2008-07029-C02-02 and CAM CONTEXTS S2009/TIC-1485.
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RD 3 may. 2024