NDLI: Nearest neighbor prediction of pareto dominance using general regression neural networks

Content Provider	IEEE Xplore Digital Library
Author	Guanqi Guo Wenjing Zeng Cheng Yin
Copyright Year	2013
Description	Author affiliation: Coll. of Inf. Sci. & Eng., Central South Univ., Changsha, China (Wenjing Zeng) \|\| Coll. of Inf. & Commun. Eng., Hunan Inst. of Sci. & Technol., Yueyang, China (Guanqi Guo) \|\| Coll. of Inf. Eng., Xiangtan Univ., Xiangtan, China (Cheng Yin)
Abstract	Conventional methods of determining Pareto dominance in multi-objective optimization evaluate and compare objective vectors of candidate solutions, but the computation and (or) experiment of evaluating objective vectors are overwhelmingly costly when computationally expensive multi-objective problems are involved. This study investigates a nearest neighbor prediction method of Pareto dominance using general regression neural networks (GRNN). The decision differential value (D-value) vector of two feasible solutions is used as the input of GRNN, and the objective D-value vector is used as the output. Under the supervision of sample candidate solutions, GRNN is used to predict objective D-value vectors between an observed solution and samples. For an observed candidate, the predicted objective D-Value vectors are used to find out the nearest neighbor samples in objective space. Experimental results show that the nearest neighbor prediction of Pareto dominance relationships using GRNN can obtain acceptable prediction accuracy. The proposed algorithm provides an effective method to relieve the curse of computation cost in computationally expensive multi-objective optimization problems, but without requirement for analytical models of objective functions.
Sponsorship	IEEE Circuits Syst. Soc.
Starting Page	439
Ending Page	443
File Size	276255
Page Count	5
File Format	PDF
ISBN	9781467352536
DOI	10.1109/FSKD.2013.6816237
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Support vector machine classification Evolutionary computation Pareto dominance nearest neighbor prediction Linear programming Prediction algorithms Vectors general regression neural network multi-objective optimization objective differential value vector Optimization
Content Type	Text
Resource Type	Article

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