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
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2018.2869001