標題: Intelligent evolutionary algorithms for large parameter optimization problems
作者: Ho, SY
Shu, LS
Chen, JH
生物資訊及系統生物研究所
Institude of Bioinformatics and Systems Biology
關鍵字: evolutionary algorithm (EA);genetic algorithm (GA);intelligent gene collector (IGC);multiobjective optimization;orthogonal experimental design
公開日期: 1-十二月-2004
摘要: This paper proposes two intelligent evolutionary algorithms IEA and IMOEA using a novel intelligent gene collector (IGC) to solve single and multiobjective large parameter optimization problems, respectively. IGC is the main phase in an intelligent recombination operator of IEA and IMOEA. Based on orthogonal experimental design, IGC uses a divide-and-conquer approach, which consists of adaptively dividing two individuals of parents into N pairs of gene segments, economically identifying the potentially better one of two gene segments of each pair, and systematically obtaining a potentially good approximation to the best one of all combinations using at most 2N fitness evaluations. IMOEA utilizes a novel generalized Pareto-based scale-independent fitness function for efficiently finding a set of Pareto-optimal solutions to a multiobjective optimization problem. The advantages of IEA and IMOEA are their simplicity, efficiency, and flexibility. It is shown empirically that IEA and IMOEA have high performance in solving benchmark functions comprising many parameters, as compared with some existing EAs.
URI: http://dx.doi.org/10.1109/TEVC.2004.835176
http://hdl.handle.net/11536/25561
ISSN: 1089-778X
DOI: 10.1109/TEVC.2004.835176
期刊: IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume: 8
Issue: 6
起始頁: 522
結束頁: 541
顯示於類別:期刊論文


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