Evolutionary Multitasking via Explicit Autoencoding

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

53 Scopus Citations
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  • Liang Feng
  • Lei Zhou
  • Jinghui Zhong
  • Abhishek Gupta
  • Yew-Soon Ong
  • A. K. Qin

Related Research Unit(s)


Original languageEnglish
Pages (from-to)3457-3470
Journal / PublicationIEEE Transactions on Cybernetics
Issue number9
Online published2 Jul 2018
Publication statusPublished - Sep 2019


Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary computation. In contrast to the traditional single-task evolutionary search, EMT conducts evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge among them. Due to the efficacy of EMT, it has attracted lots of research attentions and several EMT algorithms have been proposed in the literature. However, existing EMT algorithms are usually based on a common mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. This mode cannot make use of multiple biases embedded in different evolutionary search operators, which could give better search performance when properly harnessed. Keeping this in mind, this paper proposes an EMT algorithm with explicit genetic transfer across tasks, namely EMT via autoencoding, which allows the incorporation of multiple search mechanisms with different biases in the EMT paradigm. To confirm the efficacy of the proposed EMT algorithm with explicit autoencoding, comprehensive empirical studies have been conducted on both the single- and multi-objective multitask optimization problems.

Research Area(s)

  • Autoencoder, evolutionary optimization, knowledge transfer, multitask optimization

Citation Format(s)

Evolutionary Multitasking via Explicit Autoencoding. / Feng, Liang; Zhou, Lei; Zhong, Jinghui; Gupta, Abhishek; Ong, Yew-Soon; Tan, Kay-Chen; Qin, A. K.

In: IEEE Transactions on Cybernetics, Vol. 49, No. 9, 09.2019, p. 3457-3470.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review