Evolutionary optimization with hierarchical surrogates

Lu, Xiaofen1; Sun, Tao2; Tang, Ke1

2019-06

10.1016/j.swevo.2019.03.005

Swarm and Evolutionary Computation   影响因子和分区

2210-6502

2210-6510

The use of surrogate models provides an effective means for evolutionary algorithms (EAs) to reduce the number of fitness evaluations when handling computationally expensive problems. To build surrogate models, a modeling technique (e.g. ANN, SVM, RBF, etc.) needs to be decided first. Previous studies have shown that the choice of modeling technique can highly affect the performance of the surrogate model-assisted evolutionary search. However, one modeling technique might perform differently on different problem landscapes. Without any prior knowledge about the optimization problem to solve, it is very hard to decide which modeling technique to use. To address this issue, in this paper, we propose a novel modeling technique selection strategy in the framework of memetic algorithm (MA). The proposed strategy employs a hierarchical structure of surrogate models and can automatically choose a modeling technique from a pre-specified set of modeling techniques during the optimization process. A mathematic analysis is given to show the effectiveness of the proposed method. Moreover, experimental studies are conducted to compare the proposed method with two other modeling technique selection methods as well as three state-of-the-art optimization algorithms. Experimental results on the used benchmark test functions demonstrate the superiority of the proposed method.

Evolutionary algorithms Computationally expensive problems Surrogate model Multiple modeling techniques

SCI ; EI

英语

第一

Program for University Key Laboratory of Guangdong Province[2017KSYS008]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000474313300003

ELSEVIER

20191306715902

Benchmarking ; Optimization ; Problem solving

Optimization Techniques:921.5

Web of Science

1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Univ Key Lab Evolving Intelligent Syst Guangdong, Shenzhen Key Lab Computat Intelligence, Shenzhen 518055, Peoples R China
2.Huawei Technol, Shenzhen 518129, Guangdong, Peoples R China

Lu, Xiaofen,Sun, Tao,Tang, Ke. Evolutionary optimization with hierarchical surrogates[J]. Swarm and Evolutionary Computation,2019,47:21-32.

Lu, Xiaofen,Sun, Tao,&Tang, Ke.(2019).Evolutionary optimization with hierarchical surrogates.Swarm and Evolutionary Computation,47,21-32.

Lu, Xiaofen,et al."Evolutionary optimization with hierarchical surrogates".Swarm and Evolutionary Computation 47(2019):21-32.