## 1 Introduction
The results indicate that CEE can generate useful knowledge that traditional classifiers fail to extract. In [11], GANEL was proposed, a genetic algorithm (GA) that incorporates never-ending learning (NEL) principles for gene ontology classification. This approach aims to generate concise, high-level classification rules that capture stronger biological patterns while balancing accuracy and interpretability. The results demonstrate that GANEL outperforms CEE, showing higher accuracy with a smaller attribute set, thus enhancing biological data analysis. In [12], a new genetic algorithm operator, named Transgenic, inspired by genetically modified organisms (GMOs) was proposed. The operator artificially inserted relevant features into chromosomes, enhancing evolutionary convergence. By leveraging both a priori knowledge and historical data, Transgenic enabled GAs to identify and prioritize important attributes, leading to better performance in rule classification tasks compared to traditional GA operators. In [13], MDRGA (multiple disjunctions rule genetic algorithm) was proposed, a GA tailored for inducing non-linear IF-THEN classification rules. MDRGA is designed to improve classification accuracy while maintaining interpretability. The approach was evaluated against traditional classifiers and GA-based methods (CEE, NLCEE) on non-linear datasets, consistently achieving superior classification accuracy. In [14], the study presented a hybrid approach integrating GAs with feature selection to identify relevant gene subsets and derive high-level classification rules for cancer datasets (NCI60). The results demonstrated that the selected gene sets provided strong predictive correlations with disease classes while maintaining interpretability and surpassing traditional classification techniques in accuracy. In [15], the research proposed an architecture combining GAs and traditional classifiers (decision trees, naive Bayes) for assisting biocurators in gene ontology (GO) classification. The system balanced accuracy, interpretability, and comprehensibility, achieving high classification rates while reducing manual annotation effort. In [16], the study extended the computational evolutionary environment (CEE) by incorporating information gain (IG) for attribute selection, resulting in the IG-CEE method. IG-CEE improves classification accuracy and convergence rates by focusing on the most relevant attributes for rule construction. Comparisons of the CEE to traditional classifiers demonstrated superior classification performance. In [17], the study presented a novel approach for designing antimicrobial peptides (AMPs) by integrating GAs with rough set theory, a transparent machine learning method. The proposed system employed supervised learning and in vitro bacterial assays to optimize peptide activity against S. epidermidis, a strain associated with implant infections. This is the first application of codon-based genetic algorithms combined with rough set theory for computational peptide sequence design. In [18], the study proposed GADIC, a novel classification algorithm combining data importance (DI) reformatting and GAs to improve machine learning classifier performance. Evaluated on five classifiers (SVM, KNN, LR, DT, and NB) and seven datasets, GADIC significantly enhanced accuracy, with improvements ranging from 5.96% to 16.79%, except for a minor 1% decrease in NB on one dataset. KNN showed the highest performance gain, while LR had the lowest improvement. As noted by [PERSON] and [PERSON] [11], achieving high accuracy is essential but is not the sole objective in classification tasks. Interpretability and comprehensibility are equally critical, especially in practical applications where understanding the reasoning behind predictions is vital. In [10], a GA was proposed to mine IF-THEN understandable rules.