## 1 Introduction  
Genetic algorithms (GAs) are computational methods of search and optimization based on the principles of evolution by natural selection [1]. GAs' operators are based on the mechanisms of genetics and heredity [2], allowing for an efficient search space exploration. They are commonly used when the search space is very large, and they can often find good solutions. Data mining (DM) is an interdisciplinary domain focused on leveraging methods and tools to uncover valuable insights and patterns within large datasets. Among its diverse applications, the classification task remains one of the most extensively explored areas, aiming to assign predefined labels to data instances based on their attributes [3]. The integration of artificial intelligence (AI) and machine learning (ML) into healthcare and decision support systems has gained significant attention in recent years. [PERSON] and collaborators [4] proposed a hybrid ML approach incorporating genetic algorithms for feature selection to evaluate COVID-19 severity based on clinical and hematological parameters. Similarly, [PERSON] and collaborators [5] introduced a quantum-inspired interpretable AI system for early rheumatoid arthritis detection, emphasizing the need for explainability in AI-driven medical diagnostics. [PERSON] and [PERSON] [6] conducted an empirical study comparing post hoc explanations with directly interpretable models, demonstrating that interpretable models can often approximate black box models with comparable fidelity. These studies collectively highlight the importance of model interpretability and robustness in AI applications, reinforcing the need for explainable ML techniques in critical decision-making systems. Over the years, various classification techniques have been proposed and refined, including support vector machines (SVM) and artificial neural networks (ANN). Despite their popularity, these approaches are typically categorized as black box models, as they generate outputs without providing interpretable explanations, such as decision trees or IF-THEN rules. In recent years, several works were developed using GAs as a method for classification, as can be seen in the following descriptions:  
In [7], the study presented a hybrid classification approach that combined genetic algorithms (GAs) with the k-nearest neighbors (k-NN) algorithm. The GA was employed to optimize real-valued weights for individual attributes in the training dataset, refining feature importance in the classification process. The proposed hybrid model was evaluated on three test cases, demonstrating superior classification performance compared to the traditional k-NN algorithm in all instances. This approach highlights the effectiveness of evolutionary computation in improving distance-based classification methods. In [8], the study introduced a full-memory genetic algorithm (GA) that integrated task-specific inference rules from inductive learning. By aligning GA operations with problem-specific heuristics, the approach enhanced concept learning and domain knowledge incorporation. Initial results demonstrate its effectiveness, and the framework can be extended to other problem-solving domains. In [9], the study presented a classification algorithm based on genetic algorithms (GAs) designed to extract interpretable IF-THEN rules for data mining tasks. The proposed GA employed a flexible chromosome encoding, allowing for a fixed genotype while maintaining a variable phenotype. The effectiveness of the approach was demonstrated through experiments on medical datasets, proving its capability to generate human-readable rules for classification. In [10], the computational evolutionary environment (CEE) was introduced, a genetic algorithm-based method for extracting classification rules from biological datasets. The primary objective of CEE is to balance classification accuracy, interpretability, and comprehensibility by discovering concise, high-level rules from biological databases.