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21 July 2024 A study on the application of response surface methodology and Bayesian optimization in parameter tuning of genetic algorithms
Jiahao Chen, Richmond Afotey Nii Okle
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Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132190L (2024) https://doi.org/10.1117/12.3036921
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China
Abstract
This study investigates the application of response surface methodology and Bayesian optimization in parameter tuning of genetic algorithms and their effectiveness. The research objective is to improve the performance of genetic algorithms in solving the traveler problem (TSP), including reducing the optimal path length and improving the convergence speed. We first detailed the mathematical principles and computational procedures of response surface methodology and Bayesian optimization. Subsequently, a hypothetical experiment is designed, which includes the implementation of the genetic algorithm, parameter settings, and the determination of evaluation criteria and performance metrics. The experimental results show that both response surface methodology and Bayesian optimization can effectively improve the performance of the genetic algorithm on the TSP problem. Bayesian optimization performs more significantly in terms of the reduction of optimal path length and the improvement of convergence speed, especially when dealing with complex parameter spaces. We provide a detailed comparison of the effectiveness of the two methods and analyze their characteristics and applicable scenarios. It is shown that these two parameter optimization techniques are of great significance in improving the performance of genetic algorithms and provide new perspectives for the theoretical development and practical application of genetic algorithms. Future research directions include exploring the application of these methods to other types of optimization problems and multi-objective optimization, as well as the possibility of combining adaptive parameter tuning and machine learning techniques.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jiahao Chen and Richmond Afotey Nii Okle "A study on the application of response surface methodology and Bayesian optimization in parameter tuning of genetic algorithms", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132190L (21 July 2024); https://doi.org/10.1117/12.3036921
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KEYWORDS
Genetic algorithms
Mathematical optimization
Design
Data modeling
Process modeling
Systems modeling
Machine learning
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