In　the　presence　of　scale,　dynamism,　uncertainty,　and　elasticity,　cloud　engineers　face　several　challenges　when　allocating　resources　for　cloud-based　software　services.　They　should　allocate　appropriate　resources　in　order　to　guarantee　good　quality　of　services　as　well　as　low　cost　of　resources.　Self-adaptive　ability　is　needed　in　this　process　because　engineers＇　intervention　is　difficult.　Traditional　self-adaptive　resource　allocation　methods　are　policy-driven.　Thus,　cloud　engineers　usually　have　to　develop　separate　sets　of　rules　for　each　systems　in　order　to　allocate　resources　effectively,　which　leads　to　high　administrative　cost　and　implementation　complexity.　Machine　learning　has　made　great　achievements　in　many　fields,　and　it　can　be　also　applied　to　resource　allocation.　In　this　paper,　we　present　a　self-learning　and　self-adaptive　approach　to　resource　allocation　for　cloud-based　software　services.　For　a　given　cloud-based　software　service,　its　QoS　model　is　firstly　trained　on　history　data,　which　is　capable　to　predict　the　QoS　value　as　output　by　using　the　information　on　workload　and　allocated　resources　as　inputs.　Then,　on-line　decision-making　on　resource　allocation　can　be　carried　out　automatically　based　on　genetic　algorithm,　which　is　aimed　to　search　reasonable　resource　allocation　plan　by　using　the　QoS　model.　We　evaluate　our　approach　on　RUBiS　benchmark,　demonstrating　the　accuracy　of　the　QoS　model　over　90%　and　the　improvement　of　resource　utilization　by　10%-30%.