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This paper systematically studies how different evaluation objectives (accuracy, silhouette score, PCA reconstruction loss) and subset-size regularization directions affect search dynamics and solution quality in multiobjective unsupervised feature selection, showing that silhouette-based formulations bias toward trivial low-cardinality solutions while PCA loss yields compact subsets with competitive accuracy.