Evolutionary Multitasking via Explicit Autoencoding

Liang Feng, Lei Zhou, Jinghui Zhong, Abhishek Gupta, Yew Soon Ong, Kay Chen Tan, A. K. Qin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

230 Citations (Scopus)


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.

Original languageEnglish
Article number8401802
Pages (from-to)3457-3470
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number9
Publication statusPublished - Sept 2019
Externally publishedYes


  • Autoencoder
  • evolutionary optimization
  • knowledge transfer
  • multitask optimization

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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