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An Evolutionary Algorithm for Large-Scale Sparse Multi-Objective Optimization Problems

Tian, Ye, Zhang, Xingyi, Wang, Chao and Jin, Yaochu (2019) An Evolutionary Algorithm for Large-Scale Sparse Multi-Objective Optimization Problems IEEE Transactions on Evolutionary Computation.

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Abstract

In the last two decades, a variety of different types of multi-objective optimization problems (MOPs) have been extensively investigated in the evolutionary computation community. However, most existing evolutionary algorithms encounter difficulties in dealing with MOPs whose Pareto optimal solutions are sparse (i.e., most decision variables of the optimal solutions are zero), especially when the number of decision variables is large. Such large-scale sparse MOPs exist in a wide range of applications, for example, feature selection that aims to find a small subset of features from a large number of candidate features, or structure optimization of neural networks whose connections are sparse to alleviate overfitting. This paper proposes an evolutionary algorithm for solving large-scale sparse MOPs. The proposed algorithm suggests a new population initialization strategy and genetic operators by taking the sparse nature of the Pareto optimal solutions into consideration, to ensure the sparsity of the generated solutions. Moreover, this paper also designs a test suite to assess the performance of the proposed algorithm for large-scale sparse MOPs. Experimental results on the proposed test suite and four application examples demonstrate the superiority of the proposed algorithm over seven existing algorithms in solving large-scale sparse MOPs.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Tian, Ye
Zhang, Xingyi
Wang, Chao
Jin, YaochuYaochu.Jin@surrey.ac.uk
Date : 22 May 2019
DOI : 10.1109/TEVC.2019.2918140
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Large-scale multi-objective optimization; Evolutionary algorithm; Sparse Pareto optimal solutions; Feature selection; Evolutionary neural network; Pareto optimization; Neural networks; Feature extraction; Evolutionary computation; Training; Sociology
Depositing User : Clive Harris
Date Deposited : 29 May 2019 08:14
Last Modified : 29 May 2019 08:14
URI: http://epubs.surrey.ac.uk/id/eprint/851901

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