Intelligent evolutionary algorithms for large parameter optimization problems

Shinn-Ying Ho*, Li Sun Shu, Jian Hung Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

205 Scopus citations


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.

Original languageEnglish
Pages (from-to)522-541
Number of pages20
JournalIEEE Transactions on Evolutionary Computation
Issue number6
StatePublished - 1 Dec 2004


  • Evolutionary algorithm (EA)
  • Genetic algorithm (GA)
  • Intelligent gene collector (IGC)
  • Multiobjective optimization
  • Orthogonal experimental design

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