Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-17298-4_66
Title: Probabilistic based evolutionary optimizers in bi-objective travelling salesman problem
Authors: Shim, V.A.
Tan, K.C. 
Chia, J.Y.
Keywords: Estimation of distribution algorithm
evolutionary multi-objective optimization
restricted Boltzmann machine
travelling salesman problem
Issue Date: 2010
Source: Shim, V.A.,Tan, K.C.,Chia, J.Y. (2010). Probabilistic based evolutionary optimizers in bi-objective travelling salesman problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6457 LNCS : 588-592. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-17298-4_66
Abstract: This paper studies the probabilistic based evolutionary algorithms in dealing with bi-objective travelling salesman problem. Multi-objective restricted Boltzmann machine and univariate marginal distribution algorithm in binary representation are modified into permutation based representation. Each city is represented by an integer number and the probability distributions of the cities are constructed by running the modeling approach. A refinement operator and a local exploitation operator are proposed in this work. The probabilistic based evolutionary optimizers are subsequently combined with genetic based evolutionary optimizer to complement the limitations of both algorithms. © 2010 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/71503
ISBN: 3642172970
ISSN: 03029743
DOI: 10.1007/978-3-642-17298-4_66
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