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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Vydáno v:	IEEE transactions on evolutionary computation Ročník 24; číslo 1; s. 16 - 28
Hlavní autoři:	Zheng, Xiaolong, Qin, A. K., Gong, Maoguo, Zhou, Deyun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Cites_doi	10.1016/j.swevo.2011.02.002 10.1007/978-3-319-68759-9_38 10.1109/TEVC.2003.819944 10.1109/SSCI.2016.7850040 10.1109/CEC.2017.7969407 10.1145/3205651.3205736 10.1007/978-3-319-94472-2_10 10.1007/s11047-005-1625-y 10.1145/3205651.3205761 10.1109/TEVC.2015.2458037 10.1109/CEC.2017.7969454 10.24963/ijcai.2018/538 10.1109/CEC.2017.7969596 10.1109/SSCI.2016.7850038 10.1109/4235.585888 10.1109/CIS.2017.00050 10.1109/TCYB.2016.2554622 10.1109/CEC.2016.7744178 10.1007/978-3-319-28270-1_5 10.1109/TENCON.2016.7848632 10.1287/ijoc.6.2.154 10.1007/978-3-662-03423-1 10.1109/CEC.2017.7969579
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References	baker (ref10) 1987 ref13 ref12 ref15 ref14 ref31 ref11 ref32 ref2 ref17 cloninger (ref24) 1979; 31 ref16 ref19 ref18 deb (ref26) 1995; 9 yuan (ref9) 2017 demšar (ref30) 2006; 7 ref23 sampath (ref21) 2017; 11 ref25 ref20 ref22 zhou (ref3) 2016 da (ref7) 2017 swersky (ref1) 2013 ref28 ref27 ref29 ref8 ref4 ref6 ref5
References_xml	– volume: 11 year: 2017 ident: ref21 article-title: Evolutionary multitasking to support optimal power flow under rapid load variations publication-title: Southern Power System Technology in Chinese – ident: ref29 doi: 10.1016/j.swevo.2011.02.002 – ident: ref31 doi: 10.1007/978-3-319-68759-9_38 – volume: 31 start-page: 176 year: 1979 ident: ref24 article-title: Multifactorial inheritance with cultural transmission and assortative mating. II. A general model of combined polygenic and cultural inheritance publication-title: Amer J Human Genet – ident: ref28 doi: 10.1109/TEVC.2003.819944 – ident: ref22 doi: 10.1109/SSCI.2016.7850040 – ident: ref27 doi: 10.1109/CEC.2017.7969407 – ident: ref19 doi: 10.1145/3205651.3205736 – ident: ref23 doi: 10.1007/978-3-319-94472-2_10 – start-page: 1 year: 2016 ident: ref3 article-title: Evolutionary multitasking in combinatorial search spaces: A case study in capacitated vehicle routing problem publication-title: Proc SSSC – ident: ref5 doi: 10.1007/s11047-005-1625-y – ident: ref15 doi: 10.1145/3205651.3205761 – ident: ref2 doi: 10.1109/TEVC.2015.2458037 – ident: ref11 doi: 10.1109/CEC.2017.7969454 – ident: ref13 doi: 10.24963/ijcai.2018/538 – ident: ref12 doi: 10.1109/CEC.2017.7969596 – ident: ref16 doi: 10.1109/SSCI.2016.7850038 – year: 2017 ident: ref7 publication-title: Evolutionary Multitasking for Single-objective Continuous Optimization Benchmark Problems Performance Metric and Baseline Results – volume: 7 start-page: 1 year: 2006 ident: ref30 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J Mach Learn Res – ident: ref4 doi: 10.1109/4235.585888 – start-page: 2004 year: 2013 ident: ref1 article-title: Multi-task Bayesian optimization publication-title: Proc Adv Neural Inf Process Syst – ident: ref32 doi: 10.1109/CIS.2017.00050 – ident: ref8 doi: 10.1109/TCYB.2016.2554622 – ident: ref17 doi: 10.1109/CEC.2016.7744178 – ident: ref18 doi: 10.1007/978-3-319-28270-1_5 – ident: ref20 doi: 10.1109/TENCON.2016.7848632 – ident: ref25 doi: 10.1287/ijoc.6.2.154 – ident: ref6 doi: 10.1007/978-3-662-03423-1 – volume: 9 start-page: 115 year: 1995 ident: ref26 article-title: Simulated binary crossover for continuous search space publication-title: Complex Syst – year: 2017 ident: ref9 publication-title: Evolutionary Multitasking for Multiobjective Continuous Optimization Benchmark Problems Performance Metrics and Baseline Results – start-page: 14 year: 1987 ident: ref10 article-title: Reducing bias and inefficiency in the selection algorithm publication-title: Proc 2nd Int Conf Genet Algorithms – ident: ref14 doi: 10.1109/CEC.2017.7969579
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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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