DocumentCode :
1186659
Title :
Intelligent evolutionary algorithms for large parameter optimization problems
Author :
Ho, Shinn-Ying ; Shu, Li-Sun ; Chen, Jian-Hung
Author_Institution :
Dept. of Biol. Sci. & Technol., Nat. Chiao Tung Univ., Hsin-Chu, Taiwan
Volume :
8
Issue :
6
fYear :
2004
Firstpage :
522
Lastpage :
541
Abstract :
This work 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.
Keywords :
divide and conquer methods; evolutionary computation; 2N fitness evaluations; Pareto-based scale-independent fitness function; Pareto-optimal solution; divide-and-conquer approach; economical identification; gene segments; intelligent evolutionary algorithms; intelligent gene collector; intelligent recombination operator; large parameter optimization problem; multiobjective optimization problem; Councils; Design for experiments; Design optimization; Evolution (biology); Evolutionary computation; Genetic algorithms; Genetic mutations; Genetic programming; Helium; Optimization methods;
fLanguage :
English
Journal_Title :
Evolutionary Computation, IEEE Transactions on
Publisher :
ieee
ISSN :
1089-778X
Type :
jour
DOI :
10.1109/TEVC.2004.835176
Filename :
1369245
Link To Document :
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