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An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

In the last two decades, a variety of different types of multiobjective 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. The 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.

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Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.24 No.2 April 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 380 - 393

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

FEATURE SELECTION
EVOLUTIONARY ALGORITHM
EVOLUTIONARY NEURAL NETWORK
LARGE-SCALE MULTIOBJECTIVE OPTIMIZATION (MOP)
SPARSE PARETO OPTIMAL SOLUTIONS

Info Detail Spesifik

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Pernyataan Tanggungjawab

Ye Tian, Xingyi Zhang, Chao Wang, Yaochu Jin

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