Given　an　undirected　graph　G　=　(V,　E)　with　a　set　V　of　vertices,　and　a　set　E　of　edges　with　weights,　the　max-cut　problem　consists　of　partitioning　all　vertices　into　two　independent　sets　such　that　the　sum　of　the　weights　of　the　edges　between　different　sets　is　maximised.　The　max-cut　problem　is　an　NP-hard　problem.　An　efficient　memetic　algorithm　is　proposed　in　this　paper　for　the　problem.　The　proposed　memetic　algorithm　uses　a　local　search　procedure　and　a　new　crossover　operator　based　on　the　encoding　characteristic　of　the　max-cut　problem　to　generate　new　offsprings.　Then　the　algorithm　uses　a　function,　which　takes　into　account　both　the　solution　quality　and　the　diversity　of　population,　to　control　the　population　updating.　Experiments　were　performed　on　three　sets　of　benchmark　instances　of　size　up　to　10,000　vertices.　Experiment　results　and　comparisons　demonstrate　the　effectiveness　of　the　proposed　algorithm　in　both　solution　quality　and　computational　time.　Copyright　©　2015　Inderscience　Enterprises　Ltd.