In　this　paper,　we　present　an　adaptive　algorithm　for　maximizing　a　monotone　nonsubmodular　function　with　a　cardinality　constraint.　Based　on　the　relationship　between　OPT　and　the　maximum　marginal　gain　of　the　elements　in　the　ground　set,　the　algorithm　first　calculates　all　possible　values　of　OPT,　then　computes　in　parallel　a　family　of　sets　each　of　which　corresponding　to　each　value　of　OPT,　and　lastly　selects　the　set　with　maximum　value　as　the　desired　solution.　For　the　first,　we　divide　the　value　range　of　OPT　into　finite　parts　and　take　the　lower　bound　of　each　part　as　a　possible　value　of　OPT.　As　the　main　ingredient　for　the　parallel　computation,　we　use　the　Bernoulli　distribution　to　independently　sample　elements　so　as　to　ensure　further　parallelism.　Provided　the　generic　submodularity　ratio　(Formula　Presented)　of　the　monotone　set　function,　we　prove　the　algorithm　deserves　an　approximation　ratio　(Formula　Presented),　consumes　(Formula　Presented)　adaptive　rounds,　and　needs　(Formula　Presented)　oracle　queries　in　expectation.　Moreover,　if　the　set　function　is　submodular　(i.　e.　(Formula　Presented)),　our　algorithm　can　achieve　an　approximation　guarantee　(Formula　Presented)　coinciding　with　the　state-of-art　result.　©　2020,　Springer　Nature　Switzerland　AG.