Neural networks are widely used in the literature in a variety of fields and for a large number of applications. A major challenge in their use is the need to identify and process hyperparametric values. Grid Search is a widely used technique for meeting this task. It systematically searches for values in a predefined range of hyperparameters. However, selecting the appropriate range of hyperparameters can be difficult, as the search space can be vast, resulting in an extensive number of combinations to be tested. It is more suited to short, fast searches for hyperparameter values, within ranges that are known to be generally efficient. In this paper, we present an improvement to Grid Search using BootBOGS, a bootstrap-based approach to hyperparameter optimization. BootBOGS is a hybrid approach that combines bootstrap and Bayesian Search with the Grid Search technique to perform an efficient search in hyperparameter space. Bayesian Search is used to initialize hyperparameter ranges. Bootstrap is used to explore the distribution of model performance for each hyperparameter combination and to reduce its variance, enabling us to better understand the margins of these hyperparameters and to reduce these ranges. Grid Search is then used to refine the selection of hyperparameters. To evaluate the effectiveness of the proposed approach, a set of computational experiments are carried out on four different datasets from classification problems, for which we compared BootBOGS to several other strategies: Grid Search, Random Search, and Bayesian Optimization. The results show that our method is able to find better hyperparameter configurations in terms of predictive quality with a reasonable runtime and lead to more robust and reliable hyperparameter tuning processes.