Ensemble　learning　techniques　have　been　proven　to　be　useful　to　improve　the　performance　of　traditional　variable　selection　methods　such　as　the　lasso,　the　genetic　algorithm,　and　the　stepwise　search.　Following　the　main　principle　of　the　building　methods　for　variable　selection　ensembles,　we　propose　a　novel　approach　by　adding　noise　to　the　response　variable　so　that　the　selection　accuracy　is　maximized.　In　order　to　generate　multiple　but　slightly　different　importance　measures　for　each　variable,　Gaussian　noises　are　artificially　added　to　the　response.　The　new　training　set　(Le,　the　original　design　matrix　together　with　the　new　response　vector)　is　then　used　as　the　input　of　the　genetic　algorithm　to　perform　the　variable　selection.　By　repeating　this　process　for　a　number　of　iterations　and　fusing　the　results　by　simple　averaging,　the　variables　are　ranked　and　a　thresholding　rule　is　then　employed　to　identify　the　important　variables.　The　performance　of　the　proposed　method　is　studied　on　simulated　data　sets.　Experimental　results　demonstrate　that　the　proposed　method　yields　better　performance　than　several　other　methods.