The　max-bisection　problem　consists　in　partitioning　the　vertices　of　a　weighted　undirected　graph　into　two　equally　sized　subsets　so　as　to　maximize　the　sum　of　the　weights　of　crossing　edges.　It　is　an　NP-hard　combinatorial　optimization　problem　that　arises　in　many　applications.　In　this　paper,　we　present　a　memetic　algorithm　for　the　max-bisection　problem,　which　integrates　a　new　fast　local　search　procedure,　a　crossover　operator,　and　a　pool　updating　strategy.　These　strategies　achieve　a　balance　between　intensification　and　diversification.　Extensive　experiments　were　performed　on　a　number　of　benchmark　instances　with　800　to　10,000　vertices　from　the　literature.　The　proposed　memetic　algorithm　improved　the　best　known　solutions　for　all　benchmark　instances　tested　in　this　paper.　The　improvement　in　terms　of　cut　value　over　the　CirCut　by　Burer　et　al.　ranging　from　0.02　to　4.15　percent,　and　the　average　time　of　our　proposed　memetic　algorithm　is　much　lower　than　that　of　CirCut.　It　shows　that　the　proposed　memetic　algorithm　can　find　high　quality　solutions　in　an　acceptable　running　time.　©　2013　IEEE.