Data-Driven Evolutionary Optimization: An Overview and Case Studies

Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly phy...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 23; číslo 3; s. 442 - 458
Hlavní autori:	Jin, Yaochu, Wang, Handing, Chugh, Tinkle, Guo, Dan, Miettinen, Kaisa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.06.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Computational modeling Computer simulation Data mining Data models Data science data-driven optimization Evolutionary algorithms evolutionary algorithms (EAs) Fitness Machine learning model management Optimization Sociology Statistics surrogate Taxonomy
ISSN:	1089-778X, 1941-0026
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.
AbstractList	Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.
Author	Wang, Handing Chugh, Tinkle Miettinen, Kaisa Jin, Yaochu Guo, Dan
Author_xml	– sequence: 1 givenname: Yaochu orcidid: 0000-0003-1100-0631 surname: Jin fullname: Jin, Yaochu email: yaochu.jin@surrey.ac.uk organization: Department of Computer Science, University of Surrey, Guildford, U.K – sequence: 2 givenname: Handing orcidid: 0000-0002-4805-3780 surname: Wang fullname: Wang, Handing email: hdwang@xidian.edu.cn organization: Department of Computer Science, University of Surrey, Guildford, U.K – sequence: 3 givenname: Tinkle orcidid: 0000-0001-5123-8148 surname: Chugh fullname: Chugh, Tinkle email: t.chugh@exeter.ac.uk organization: Department of Computer Science, University of Exeter, Exeter, U.K – sequence: 4 givenname: Dan orcidid: 0000-0002-0334-0145 surname: Guo fullname: Guo, Dan email: guodan717@163.com organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China – sequence: 5 givenname: Kaisa orcidid: 0000-0003-1013-4689 surname: Miettinen fullname: Miettinen, Kaisa email: kaisa.miettinen@jyu.fi organization: Faculty of Information Technology, University of Jyvaskyla, Finland
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Snippet	Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world...
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SubjectTerms	Computational modeling Computer simulation Data mining Data models Data science data-driven optimization Evolutionary algorithms evolutionary algorithms (EAs) Fitness Machine learning model management Optimization Sociology Statistics surrogate Taxonomy
Title	Data-Driven Evolutionary Optimization: An Overview and Case Studies
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