Publication: Moving away from error-based learning in multi-objective estimation of distribution algorithms
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2010-07
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ACM
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Abstract
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.
Description
Proceedings of: 12th annual conference on Genetic and evolutionary computation
(GECCO '10). Portland, Oregon, USA, July 7-11, 2010.
Keywords
Multi-objective Optimization, Estimation of Distribution Algorithms, Adaptive Resonance Theory
Bibliographic citation
GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation. (2010) ACM, New York, USA, 545-546