Stochastic Schemata Exploiter-Based Optimization of Hyper-parameters for XGBoost


XGBoost is well-known as an open-source software library that provides a regularizing gradient boosting framework. Although it is widely used in the machine learning field, its performance depends on the determination of hyper-parameters. This study focuses on the optimization algorithm for hyper-parameters of XGBoost by using Stochastic Schemata Exploiter (SSE). SSE, which is one of Evolutionary Algorithms, is successfully applied to combinatorial optimization problems. SSE is applied for optimizing hyper-parameters of XGBoost in this study. The original SSE algorithm is modified for hyper-parameter optimization. When comparing SSE with a simple Genetic Algorithm, there are two interesting features: quick convergence and a small number of control parameters. The proposed algorithm is compared with other hyper-parameter optimization algorithms such as Gradient Boosted Regression Trees (GBRT), Tree-structured Parzen Estimator (TPE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and Random Search in order to confirm its validity. The numerical results show that SSE has a good convergence property, even with fewer control parameters than other methods.


evolutionary computation, Stochastic Schemata Exploiter, hyper-parameter optimization, XGBoost,


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Feb 29, 2024
How to Cite
MAKINO, Hiroya; KITA, Eisuke. Stochastic Schemata Exploiter-Based Optimization of Hyper-parameters for XGBoost. Computer Assisted Methods in Engineering and Science, [S.l.], v. 31, n. 1, p. 113–132, feb. 2024. ISSN 2956-5839. Available at: <>. Date accessed: 17 apr. 2024. doi: