Selecting the best features in a dataset improves accuracy and efficiency of classifiers in a learning process. Datasets generally have more features than necessary, some of them being irrelevant or redundant to others. For this reason, numerous feature selection methods have been developed, in which different evaluation functions and measures are applied. This paper proposes the systematic application of individual feature evaluation methods to initialize search-based feature subset selection methods. An exhaustive review of the starting methods used by genetic algorithms from 2014 to 2020 has been carried out. Subsequently, an in-depth empirical study has been carried out evaluating the proposal for different search-based feature selection methods (Sequential forward and backward selection, Las Vegas filter and wrapper, Simulated Annealing and Genetic Algorithms). Since the computation time is reduced and the classification accuracy with the selected features is improved, the initialization of feature selection proposed in this work is proved to be worth considering while designing any feature selection algorithms.

The circadian rhythm plays a crucial role in regulating biological processes, and its disruption is linked to various health issues. Identifying small molecules that influence the circadian period is essential for developing targeted therapies. This study explores the use of evolutionary optimization techniques to enhance the classification of these molecules. We applied a genetic algorithm to optimize feature selection and classification performance. Several tree-based learning classification algorithms (Decision Trees, Extra Trees, Random Forest, XGBoost) and a distance-based classifier (kNN) were employed. Their performance was evaluated using accuracy and F1-score, while considering their generalization ability with a validation set. The findings demonstrate that the proposed genetic algorithm improves classification accuracy and reduces overfitting compared to baseline models. Additionally, the use of variance in accuracy as a penalty factor may enhance the model’s reliability for real-world applications. Our study confirms that evolutionary optimization is an effective strategy for classifying small molecules regulating the circadian rhythm. The proposed approach not only improves predictive performance but also ensures a more robust model.

Feature selection is essential in classification tasks to reduce dimensionality, improve learning efficiency, and enhance model interpretability. This paper presents a Simple Cooperative Coevolutionary Genetic Algorithm for Feature Selection (SCCGA-FS), a minimal yet effective cooperative coevolutionary approach. The method applies a divide-and-conquer strategy, splitting the feature space into multiple species that evolve independently through standard genetic operators while cooperating to form complete solutions. A wrapper-based fitness function balances classification performance and feature reduction. SCCGA-FS was extensively evaluated on twenty-two benchmark datasets of medium and high dimensionality using k-Nearest Neighbors and Random Forest classifiers. Experimental results show that SCCGA-FS achieves competitive or superior classification accuracy compared with recent coevolutionary and swarm-based feature selection methods, while consistently selecting fewer features. The algorithm’s simplicity, interpretability, and inherent parallelization capabilities make it a robust and efficient baseline for future research in cooperative coevolutionary feature selection.