Please use this identifier to cite or link to this item: https://doi.org/10.1109/TEVC.2013.2241768
Title: An energy-based sampling technique for multi-objective restricted boltzmann machine
Authors: Shim, V.A.
Tan, K.C. 
Cheong, C.Y.
Keywords: Estimation of distribution algorithms (EDAs)
Evolutionary gradient search
Genetic algorithm (GA)
Multi-objective (MO) optimization
Restricted Boltzmann machine
Sampling technique
Issue Date: Dec-2013
Source: Shim, V.A., Tan, K.C., Cheong, C.Y. (2013-12). An energy-based sampling technique for multi-objective restricted boltzmann machine. IEEE Transactions on Evolutionary Computation 17 (6) : 767-785. ScholarBank@NUS Repository. https://doi.org/10.1109/TEVC.2013.2241768
Abstract: Estimation of distribution algorithms are gaining increased research interest due to their advantage in exploiting linkage information. This paper examines the sampling techniques of a restricted Boltzmann machine-based multi-objective (MO) estimation of distribution algorithm (REDA). The behaviors of the sampling techniques in terms of energy levels are rigorously investigated, and a sampling mechanism that exploits the energy information of the solutions in a trained network is proposed to improve the search capability of the algorithm. The REDA is then hybridized, with a genetic algorithm and a local search based on an evolutionary gradient approach, to enhance the exploration and exploitation capabilities of the algorithm. Thirty-one benchmark test problems, which consist of different difficulties and characteristics, are used to examine the efficiency of the proposed algorithm. Empirical studies show that the proposed algorithm gives promising results in terms of inverted generational distance and nondominance ratio in most of the test problems. © 1997-2012 IEEE.
Source Title: IEEE Transactions on Evolutionary Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/55012
ISSN: 1089778X
DOI: 10.1109/TEVC.2013.2241768
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