Android　is　currently　one　of　the　most　popular　mobile　operating　systems　in　the　world.　The　popularity　and　flexibility　of　the　Android　platform　makes　it　a　prime　target　for　malicious　attackers.　Android　malware　with　malicious　charges　and　privacy　theft　poses　a　serious　threat　to　users＇　security.　With　the　rapid　update　of　Android　apps　and　the　growing　number　of　malware　types,　existing　Android　app　stores　all　lack　comprehensive　malware　detection　methods　and　prevention　mechanisms.　Previous　studies　have　shown　that　the　detection　methods　based　on　feature　codes　are　easily　bypassed　by　deformed　malicious　codes　and　rely　on　the　feature　code　database.　However,　the　detection　method　based　on　machine　learning　can　deal　with　different　varieties　of　malicious　code　to　some　extent,　but　this　method　needs　to　transform　the　application　program　into　feature　vector　through　feature　engineering,　and　then　build　a　classifier　to　detect　and　classify　the　malware　quickly.　In　the　field　of　machine　learning,　the　feature　information　selected　by　traditional　feature　selection　methods　suffers　from　low　contribution　value　and　high　redundancy,　which　leads　to　the　low　accuracy　of　detection　models.　The　classical　genetic　algorithm　also　has　disadvantages　such　as　slow　convergence　speed,　poor　local　search　ability,　and　easily　influenced　by　parameters.　Therefore,　this　paper　proposes　an　improved　hybrid　adaptive　genetic　algorithm　(HAGA)　as　feature　selection　and　combines　with　a　machine　learning　classifier,　and　then　uses　the　accuracy　of　the　classifier　as　an　evaluation　criterion　to　provide　an　effective　method　for　Android　malware　detection.　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.