Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72617
Title: Evolutionary algorithm with dynamic population size for multi-objective optimization
Authors: Khor, E.F.
Tan, K.C. 
Wang, M.L.
Lee, T.H. 
Issue Date: 2000
Source: Khor, E.F.,Tan, K.C.,Wang, M.L.,Lee, T.H. (2000). Evolutionary algorithm with dynamic population size for multi-objective optimization. IECON Proceedings (Industrial Electronics Conference) 4 : 2768-2773. ScholarBank@NUS Repository.
Abstract: This paper presents a novel "incremental" multi-objective evolutionary algorithm with dynamic population size that is adaptively computed according to the on-line discovered trade-off surface and its desired population distribution density. It incorporates the method of fuzzy boundary local perturbation with interactive local fine-tuning for broader neighborhood exploration to achieve better convergence as well as discovering any gaps or missing trade-off regions and each generation. The effectiveness of the proposed methodology is validated upon a benchmark multi-objective optimization problem.
Source Title: IECON Proceedings (Industrial Electronics Conference)
URI: http://scholarbank.nus.edu.sg/handle/10635/72617
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