NDLI: A grouping-based evolutionary algorithm for constrained optimization problem

Content Provider	IEEE Xplore Digital Library
Author	Ming Yuchi Jong-Hwan Kim
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea (Ming Yuchi; Jong-Hwan Kim)
Abstract	Most of the existing evolutionary algorithms for constrained problems derate the importance of the infeasible individuals. In these algorithms, feasible individuals might get more possibility to survive and reproduce than infeasible individuals. To recover the utility of infeasible individuals, a grouping-based evolutionary algorithm (GEA) for constrained problems is proposed in this paper. Feasible population and infeasible individuals are separated as two groups. Evaluation, rank and reproduction of these groups are performed separately. The only chance for the two groups to exchange information happens when the offspring replace the parents. Thus, the designer could pay more attention to the evolutionary process inside the group. The simulation results of four benchmark problems show the effectiveness of the proposed algorithm.
Sponsorship	Air Force Iffice of Sci. Res. Asian Office of Aerospace Res and Development Army Res. Office - Far East CISCO Evoluationary Programming Soc. IEE Inst. of Engineers, Australia IEEE Neural Networks Soc. Univ. of New South Wales
Starting Page	1507
Ending Page	1512
File Size	379226
Page Count	6
File Format	PDF
ISBN	0780378040
DOI	10.1109/CEC.2003.1299851
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-12-08
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation Constraint optimization Lagrangian functions Computer science Performance evaluation Stochastic processes Cost function Sun Decoding Production
Content Type	Text
Resource Type	Article

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