Online algorithm configuration for differential evolution algorithm

The performance of evolutionary algorithms (EAs) is strongly affected by their configurations. Thus, algorithm configuration (AC) problem, that is, to properly set algorithm’s configuration, including the operators and parameter values for maximizing the algorithm’s performance on given problem(s) i...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 52; no. 8; pp. 9193 - 9211
Main Authors: Huang, Changwu, Bai, Hao, Yao, Xin
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2022
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Configurations
Evolutionary algorithms
Evolutionary computation
Machines
Manufacturing
Mechanical Engineering
Multi-armed bandit problems
Optimization
Parameter sensitivity
Processes
Search process
Sensitivity analysis
Automatic algorithm configuration
Adaptive parameter control
Differential evolution algorithm
Kernel density estimation
Multi-armed bandit
Machine learning
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary:The performance of evolutionary algorithms (EAs) is strongly affected by their configurations. Thus, algorithm configuration (AC) problem, that is, to properly set algorithm’s configuration, including the operators and parameter values for maximizing the algorithm’s performance on given problem(s) is an essential and challenging task in the design and application of EAs. In this paper, an online algorithm configuration (OAC) approach is proposed for differential evolution (DE) algorithm to adapt its configuration in a data-driven way. In our proposed OAC, the multi-armed bandit algorithm is adopted to select trial vector generation strategies for DE, and the kernel density estimation method is used to adapt the associated control parameters during the evolutionary search process. The performance of DE algorithm using the proposed OAC (OAC-DE) is evaluated on a benchmark set of 30 bound-constrained numerical optimization problems and compared with several adaptive DE variants. Besides, the influence of OAC’s hyper-parameter on its performance is analyzed. The comparison results show OAC-DE achieves better average performance than the compared algorithms, which validates the effectiveness of the proposed OAC. The sensitivity analysis indicates that the hyper-parameter of OAC has little impact on OAC-DE’s performance.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-021-02752-1