MOELIGA: a multi-objective evolutionary approach for feature selection with local improvement
Selecting the most relevant or informative features is a key issue in actual machine learning problems. Since an exhaustive search is not feasible even for a moderate number of features, an intelligent search strategy must be employed for finding an optimal subset, which implies considering how features interact with each other in promoting class separability. Balancing feature subset size and classification accuracy constitutes a multi-objective optimization challenge. Here we propose MOELIGA, a multi-objective genetic algorithm incorporating an evolutionary local improvement strategy that evolves subordinate populations to refine feature subsets. MOELIGA employs a crowding-based fitness...
| Authors | Leandro Vignolo, Matias Gerard |
|---|---|
| Primary category | cs.NE |
| Published | 21 March 2026 |
| Last updated at source | 7 April 2026 |
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