期刊
INFORMATION SCIENCES
卷 562, 期 -, 页码 414-437出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2021.03.002
关键词
Evolutionary algorithms; Surrogate models; Absolute fitness models; Relative fitness models; Expensive optimization problems
资金
- National Natural Science Foundation of China [61976111]
- Guangdong Provincial Key Laboratory [2020B121201001]
- Program for Guangdong Introducing Innovative and Enterpreneurial Teams [2017ZT07X386]
- Shenzhen Science and Technology Program [KQTD2016112514355531]
This paper provides a systematic review and comprehensive empirical study of surrogate models used in single-objective SAEAs, introducing a new taxonomy and comparing the characteristics of different models through experiments. The results are helpful for researchers to select suitable surrogate models when designing SAEAs.
Surrogate-assisted evolutionary algorithms (SAEAs), which use efficient surrogate models or meta-models to approximate the fitness function in evolutionary algorithms (EAs), are effective and popular methods for solving computationally expensive optimization problems. During the past decades, a number of SAEAs have been proposed by combining different surrogate models and EAs. This paper dedicates to providing a more systematical review and comprehensive empirical study of surrogate models used in single-objective SAEAs. A new taxonomy of surrogate models in SAEAs for single-objective optimization is introduced in this paper. Surrogate models are classified into two major categories: absolute fitness models, which directly approximate the fitness function values of candidate solutions, and relative fitness models, which estimates the relative rank or preference of candidates rather than their fitness values. Then, the characteristics of different models are analyzed and compared by conducting a series of experiments in terms of time complexity (execution time), model accuracy, parameter influence, and the overall performance when used in EAs. The empirical results are helpful for researchers to select suitable surrogate models when designing SAEAs. Open research questions and future work are discussed at the end of the paper. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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