Collinear　and　nonlinear　characteristics　of　modeling　data　have　to　be　addressed　for　constructing　effective　soft　measuring　models.　Latent　variables　(LVs)-based　modeling　approaches,　such　as　kernel　partial　least　squares　(KPLS),　can　overcome　these　disadvantages　in　certain　degree.　Selective　ensemble　(SEN)　modeling　can　improve　generalization　performance　of　learning　models　further.　Nevertheless,　how　to　select　SEN　model＇s　learning　parameters　is　an　important　open　issue.　In　this　paper,　a　novel　SENKPLS　modeling　method　based　on　double-layer　genetic　algorithm　(DLGA)　optimization　is　proposed.　At　first,　one　mechanism,　titled　outside　layer　adaptive　GA　(AGA)　optimization　encoding　and　decoding　principle,　is　employed　to　produce　initial　learning　parameter　values　for　KPLS-based　candidate-sub-models.　Then,　ensemble　sub-models　are　selected　and　combined　based　on　inside　layer　GA　optimization　toolbox　(GAOT)　and　adaptive　weighting　fusion　(AWF)　algorithm.　Thus,　SEN　models　of　all　AGA　populations　are　obtained.　Finally,　outside　layer　AGA　optimization　operations,　i.e.,　selection,　crossover　and　mutation　processes,　are　repeated　until　the　pre-set　stopping　criterion　is　satisfied.　Simulation　results　validate　the　effectiveness　of　the　proposed　method　as　far　as　the　synthetic　data,　low　dimensional　and　high　dimensional　benchmark　data.