A Preliminary Study of Adaptive Task Selection in Explicit Evolutionary Many-Tasking

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

3 Scopus Citations
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Author(s)

  • Q. Shang
  • L. Zhang
  • L. Feng
  • Y. Hou
  • J. Zhong
  • A. Gupta
  • H.-L. Liu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation (CEC)
PublisherInstitute of Electrical and Electronics Engineers
Pages2153-2159
ISBN (Electronic)9781728121536
ISBN (Print)9781728121543
Publication statusPublished - Jun 2019

Publication series

NameIEEE Congress on Evolutionary Computation, CEC - Proceedings

Conference

Title2019 IEEE Congress on Evolutionary Computation, CEC 2019
PlaceNew Zealand
CityWellington
Period10 - 13 June 2019

Abstract

Recently, evolutionary multi-tasking (EMT) has been proposed as a new evolutionary search paradigm that op-timizes multiple problems simultaneously. Due to the knowledge transfer across optimization tasks occurs along the evolutionary search process, EMT has been demonstrated to outperform the traditional single-task evolutionary search algorithms on many complex optimization problems, such as multimodal continuous optimization problems, NP-hard combinatorial optimization problems, and constrained optimization problems. Today, EMT has attracted lots of attentions, and many EMT algorithms have been proposed in the literature. The explicit EMT algorithm (EEMTA) is a recent proposed new EMT algorithm. In contrast to most of existing EMT algorithms, which employ a single population using unified space and common search operators for solving multiple problems, the EEMTA uses multiple populations which possess problem-specific solution representations and search mechanisms for different problems in evolutionary multi-tasking, which thus could lead to enhanced optimization performance. However, the original EEMTA was proposed for solving only two tasks. As knowledge transfer from inappropriate tasks may lead to negative effect on the evolutionary optimization process, additional designs of identifying task pairs for knowledge transfer is necessary in EEMTA for evolutionary multi-tasking with tasks more than two. To the best of our knowledge, there is no research effort has been conducted on this issue. Keeping this in mind, in this paper, we present a preliminary study on the task selection in EEMTA for many-task optimization. As task similarity may lose to capture the usefulness between tasks in evolutionary search, instead of using similarity measures for task selection, here we propose a credit assignment approach for selecting proper task to conduct knowledge transfer in explicit evolutionary many-tasking. The proposed approach is based on the feedbacks from the transferred solutions across tasks, which is adaptively updated along the evolutionary search. To confirm the efficacy of the proposed method, empirical studies on the many-task optimization problem, which consists of 7 commonly used optimization benchmarks, have been presented and discussed.

Citation Format(s)

A Preliminary Study of Adaptive Task Selection in Explicit Evolutionary Many-Tasking. / Shang, Q.; Zhang, L.; Feng, L.; Hou, Y.; Zhong, J.; Gupta, A.; Tan, K. C.; Liu, H.-L.

2019 IEEE Congress on Evolutionary Computation (CEC). Institute of Electrical and Electronics Engineers, 2019. p. 2153-2159 8789909 (IEEE Congress on Evolutionary Computation, CEC - Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review