The　minimum　weight-dominating　set　(MWDS)　problem　is　NP-hard　and　has　a　lot　of　applications　in　the　real　world.　Several　metaheuristic　methods　have　been　developed　for　solving　the　problem　effectively,　but　suffering　from　high　CPU　time　on　large-scale　instances.　In　this　paper,　we　design　an　effective　hybrid　memetic　algorithm　(HMA)　for　the　MWDS　problem.　First,　the　MWDS　problem　is　formulated　as　a　constrained　0-1　programming　problem　and　is　converted　to　an　equivalent　unconstrained　0-1　problem　using　an　adaptive　penalty　function.　Then,　we　develop　a　memetic　algorithm　for　the　resulting　problem,　which　contains　a　greedy　randomized　adaptive　construction　procedure,　a　tabu　local　search　procedure,　a　crossover　operator,　a　population-updating　method,　and　a　path-relinking　procedure.　These　strategies　make　a　good　tradeoff　between　intensification　and　diversification.　A　number　of　experiments　were　carried　out　on　three　types　of　instances　from　the　literature.　Compared　with　existing　algorithms,　HMA　is　able　to　find　high-quality　solutions　in　much　less　CPU　time.　Specifically,　HMA　is　at　least　six　times　faster　than　existing　algorithms　on　the　tested　instances.　With　increasing　instance　size,　the　CPU　time　required　by　HMA　increases　much　more　slowly　than　required　by　existing　algorithms.