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Data-Driven Evolutionary Optimization: An Overview and Case Studies

Jin, Yaochu, Wang, Handing, Chugh, Tinkle, Guo, Dan and Miettinen, Kaisa (2018) Data-Driven Evolutionary Optimization: An Overview and Case Studies IEEE Transactions on Evolutionary Computation.

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Jin, YaochuYaochu.Jin@surrey.ac.uk
Wang, Handinghanding.wang@surrey.ac.uk
Chugh, Tinkle
Guo, Dan
Miettinen, Kaisa
Date : 6 September 2018
DOI : 10.1109/TEVC.2018.2869001
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Data-driven optimization, evolutionary algorithms, surrogate, model management, data science, machine learning
Depositing User : Melanie Hughes
Date Deposited : 05 Sep 2018 14:37
Last Modified : 23 Nov 2018 17:42
URI: http://epubs.surrey.ac.uk/id/eprint/849220

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