The control variables of the classifiers are called hyperparameters, which aim to define relevant issues regarding the model to be trained, such as the number of estimators in a random forest algorithm or the number of layers hidden in a neural network [21]. In a neural network context, parameters are adjusted during the training phase using weights. Hyperparameters are variables set before the training, such as the network topology or learning information [22]. In this context, we previously proposed exploiting students' interaction counts solely over time (and other attributes derived from the counts) to predict at-risk students [23; 24; 25]. This approach was tested and produced good results, allowing the early prediction of students at risk of dropout and achieving overall accuracies varying from 65% to 90% in the first eight weeks of a two-year distance courses. These studies produced results comparable to those in the literature. For instance,Jayaprakash et al. [26] obtained general accuracies varying from 73% to 94% and [PERSON] et al. [4] reported accuracies from 62.22% to 67.77%. Maximizing the results obtained by predictions is a considerable challenge [27], as the different algorithms commonly present a wide variation in the performance rates that depend on the combination of several characteristics (e.g., balance among classes, amount of data, input variables, and others) and algorithm hyperparameters [28]. Evolutionary computation, and especially genetic algorithms (GAs), are used for optimization problems and tuning classifiers in several areas such as medicine [20] and emotion recognition [29], producing significant results. Here, we propose the use of an evolutionary GA to tune the hyperparameters of the classifiers, thereby optimizing the performance of the models for the early detection of students at risk of dropping out. This paper is a continuation of these previous works, now aiming to enhance the results by applying an approach that uses GAs to tune machine learning algorithms' hyperparameters. This paper contrasts the results of two methods for hyperparameter optimization applied on models to detect at-risk students in technical e-learning courses based on the counting of students' interactions inside the VLE. The first method for hyperparameter optimization is based on a GA created by the authors, and the second is the traditional widely used method called grid search [21]. During this study, we aimed to answer the following research questions:  
**RQ1. **: Does the GA approach to hyperparameter optimization outperform traditional techniques? **RQ2. **: Does the resulting predictive models generated by the use of the GA approach for hyperparameter optimization perform better than models with default hyperparameters? The remainder of this paper is organized as follows: Section 2 presents the theoretical background and related work about the problem of predicting at-risk students and the use of GAs in this context. Section 3 presents the case study conducted to test the proposed solution, detailing the data gathered, the methodology, the proposed GA for fine-tuning, and the experiments. Section 4 discusses the results, and Section 5 concludes the paper and proposes future work.