Self-Regulated Evolutionary Multitask Optimization

Evolutionary multitask optimization (EMTO) is a newly emerging research area in the field of evolutionary computation. It investigates how to solve multiple optimization problems (tasks) at the same time via evolutionary algorithms (EAs) to improve on the performance of solving each task independent...

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Published in:	IEEE transactions on evolutionary computation Vol. 24; no. 1; pp. 16 - 28
Main Authors:	Zheng, Xiaolong, Qin, A. K., Gong, Maoguo, Zhou, Deyun
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Evolutionary algorithm (EA) Evolutionary algorithms Knowledge acquisition Knowledge management Knowledge transfer multitask optimization (MTO) Multitasking Optimization Research and development Sociology Solution space Statistics Task analysis
ISSN:	1089-778X, 1941-0026
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Abstract	Evolutionary multitask optimization (EMTO) is a newly emerging research area in the field of evolutionary computation. It investigates how to solve multiple optimization problems (tasks) at the same time via evolutionary algorithms (EAs) to improve on the performance of solving each task independently, assuming if some component tasks are related then the useful knowledge (e.g., promising candidate solutions) acquired during the process of solving one task may assist in (and also benefit from) solving the other tasks. In EMTO, task relatedness is typically unknown in advance and needs to be captured via EA's population. Since the population of an EA can only cover a subregion of the solution space and keeps evolving during the search, thus captured task relatedness is local and dynamic. The multifactorial EA (MFEA) is one of the most representative EMTO techniques, inspired by the bio-cultural model of multifactorial inheritance, which transmits both biological and cultural traits from the parents to the offspring. MFEA has succeeded in solving various multitask optimization (MTO) problems. However, the intensity of knowledge transfer in MFEA is determined via its algorithmic configuration without considering the degree of task relatedness, which may prevent the effective sharing and utilization of the useful knowledge acquired in related tasks. To address this issue, we propose a self-regulated EMTO (SREMTO) algorithm to automatically adapt the intensity of cross-task knowledge transfer to different and varying degrees of relatedness between different tasks as the search proceeds so that the useful knowledge in common for solving related tasks can be captured, shared, and utilized to a great extent. We compare SREMTO with MFEA and its variants as well as the single-task optimization counterpart of SREMTO on two MTO test suites, which demonstrates the superiority of SREMTO.
AbstractList	Evolutionary multitask optimization (EMTO) is a newly emerging research area in the field of evolutionary computation. It investigates how to solve multiple optimization problems (tasks) at the same time via evolutionary algorithms (EAs) to improve on the performance of solving each task independently, assuming if some component tasks are related then the useful knowledge (e.g., promising candidate solutions) acquired during the process of solving one task may assist in (and also benefit from) solving the other tasks. In EMTO, task relatedness is typically unknown in advance and needs to be captured via EA's population. Since the population of an EA can only cover a subregion of the solution space and keeps evolving during the search, thus captured task relatedness is local and dynamic. The multifactorial EA (MFEA) is one of the most representative EMTO techniques, inspired by the bio-cultural model of multifactorial inheritance, which transmits both biological and cultural traits from the parents to the offspring. MFEA has succeeded in solving various multitask optimization (MTO) problems. However, the intensity of knowledge transfer in MFEA is determined via its algorithmic configuration without considering the degree of task relatedness, which may prevent the effective sharing and utilization of the useful knowledge acquired in related tasks. To address this issue, we propose a self-regulated EMTO (SREMTO) algorithm to automatically adapt the intensity of cross-task knowledge transfer to different and varying degrees of relatedness between different tasks as the search proceeds so that the useful knowledge in common for solving related tasks can be captured, shared, and utilized to a great extent. We compare SREMTO with MFEA and its variants as well as the single-task optimization counterpart of SREMTO on two MTO test suites, which demonstrates the superiority of SREMTO.
Author	Zheng, Xiaolong Qin, A. K. Gong, Maoguo Zhou, Deyun
Author_xml	– sequence: 1 givenname: Xiaolong surname: Zheng fullname: Zheng, Xiaolong email: xlzheng@mail.nwpu.edu.cn organization: School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China – sequence: 2 givenname: A. K. orcidid: 0000-0001-6631-1651 surname: Qin fullname: Qin, A. K. email: kqin@swin.edu.au organization: Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, VIC, Australia – sequence: 3 givenname: Maoguo orcidid: 0000-0002-0415-8556 surname: Gong fullname: Gong, Maoguo email: gong@ieee.org organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, China – sequence: 4 givenname: Deyun surname: Zhou fullname: Zhou, Deyun email: dyzhounpu@nwpu.edu.cn organization: School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China
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SubjectTerms	Evolutionary algorithm (EA) Evolutionary algorithms Knowledge acquisition Knowledge management Knowledge transfer multitask optimization (MTO) Multitasking Optimization Research and development Sociology Solution space Statistics Task analysis
Title	Self-Regulated Evolutionary Multitask Optimization
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