Inter-subject　variability　and　seat　conditions　are　complex　and　may　affect　the　dynamic　responses　of　the　occupant-seat　system　to　vibration.　This　research　was　aimed　to　clarify　the　contributions　associated　with　the　occupant　and　the　seat　to　seat　transmissibilities　and　thus　developed　an　optimized　artificial　neural　network　model　with　the　genetic　algorithm　to　represent　the　cross-axis　coupling　and　nonlinearity　of　seat　transmissibilities　with　various　seat　conditions.　Different　vibration　magnitudes,　backrest　inclinations,　cushion　thicknesses,　frequencies　and　the　mass　of　12　subjects　were　set　as　the　input　parameters　to　predict　the　vertical　in-line　and　horizontal　cross-axis　transmissibilities.　For　the　predictive　performance　metrics　(RMSE　and　R2),　the　mean　values　(0.118　and　0.889)　were　obtained　for　both　seat　transmissibilities　within　the　testing　data　sets　from　BP-ANN　models,　and　those　with　GA-BP-ANN　models　were　optimized　with　0.072　and　0.947,　respectively.　The　seat　transmissibility　predicted　from　the　model　exhibited　resonance　behavior　similar　to　that　observed　in　the　whole-body　vibration　test.　With　the　optimization　of　the　genetic　algorithm,　GA-BP-ANN　models　can　provide　enhanced　predictions　of　the　cross-axis　coupling　and　nonlinearity　of　seat　transmissibilities　when　compared　to　BP-ANN　models.　©　2024　Elsevier　B.V.