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Title: Evolutionary multitasking via explicit autoencoding
Authors: Feng, Liang
Zhou, Lei
Zhong, Jinghui
Gupta, Abhishek
Ong, Yew-Soon
Tan, Kay-Chen
Qin, A. K.
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Feng, L., Zhou, L., Zhong, J., Gupta, A., Ong, Y.-S., Tan, K.-C., & Qin, A. K. (2019). Evolutionary multitasking via explicit autoencoding. IEEE Transactions on Cybernetics, 49(9), 3457-3470. doi:10.1109/TCYB.2018.2845361
Journal: IEEE Transactions on Cybernetics
Abstract: 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 singleand multi-objective multitask optimization problems.
ISSN: 2168-2267
DOI: 10.1109/TCYB.2018.2845361
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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