A Hybrid Genetic Algorithm with Information-Theoretic Local Search for Unsupervised Feature Selection
A hybrid GA that integrates an information-theoretic local search strategy for unsupervised FS and effectively mitigates the premature convergence problem of conventional GAs and improves search efficiency and solution quality compared to traditional heuristic and evolutionary approaches is proposed.