## 1 Introduction  
Machine learning (ML) techniques are increasingly being used in a wide range of research areas including for object detection and classification [1, 2], natural language processing and data-driven control [3]. Despite the relatively good performance obtained, selecting the optimal hyperparameter (HP) values for most ML models remains a significant challenge. These hyperparameters are usually classified as either continuous, discrete, or a combination of the two [4]. Continuous hyperparameter metrics that can be optimized include the learning rate, decay, momentum and regularization parameters [5, 6]. Similarly, the number of layers in deep learning models and the number of trees in the random forest algorithm are examples of discrete hyperparameters that must be fine-tuned [7].  
We could perhaps note that other conditional search spaces might be influenced by other hyperparameters, such as the type of optimizer used in training, which determines the types and number of hyperparameters to be used [8].  
In terms of hyperparameter tuning, the random and grid search approaches are currently the most popular methods for fine-tuning the hyperparameters of many ML models [9; 10]. However, because they tend to search every sample point in the hyperparameter space, regardless of the training configuration used, these search techniques are regarded as naive approaches. Consequently, such search methods are inefficient, and the computational complexity and cost of optimizing hyperparameter values increase. Several other optimization techniques, such as Bayesian optimization, evolutionary strategies, and tree pipeline approaches have been proposed for searching and selecting the optimal hyperparameter values of many ML models [11; 12; 13].  
It has been demonstrated that the Bayesian optimization technique with Gaussian processes can effectively search for hyperparameter values in a continuous search space [14]. Tree-based Bayesian optimization and evolutionary algorithms have also been demonstrated to be flexible and applicable in a wide range of search spaces [13]. However, comparing the performance of different ML models is necessary, especially when different optimization techniques are used to optimize the hyperparameter values under different application conditions. As a result, in order to improve the training efficiency and accuracy performance of ML models, the present article proposes an improved optimization technique called the simple deterministic selection genetic algorithm (SDSGA) for hyperparameter optimization. Following the performance of the proposed technique, we summarize the current article's contributions as follows:  
1. A simple deterministic selection genetic algorithm (SDSGA) was developed as an improved optimization technique for computing optimal HP values with a small number of fitness evaluations.
2. The proposed SDSGA has shown a high exploitative capability which makes it able to search more deeply within a region in the search space without sacrificing its exploratory capabilities.  
The rest of the paper is structured as follows: Section 2 examines the related work, while our proposed SDSGA is described in Section 3. Our method of comparison and analysis is presented in Section 4. Experimental results were obtained, discussion is presented in Section 5, and Conclusions are drawn in Section 6.