In　this　paper　we　presented　a　novel　hybrid　genetic　algorithm　for　solving　NLP　problems　based　on　combining　the　Genetic　algorithm　and　Simulated　annealing,　together　with　a　local　search　strategy.　The　proposed　hybrid　approach　combines　the　merits　of　genetic　algorithm　(GA)　with　simulated　annealing　(SA)　to　construct　a　more　efficient　genetic　simulated　annealing　(GSA)　algorithm　for　global　search,　which　could　well　maintain　the　population　diversity　in　GA　evolution　without　becoming　easily　trapped　in　local　optimum.　The　iterative　hill　climbing　(IHC)　method　as　a　local　search　technique　is　incorporated　into　GSA　loop　to　speed　up　the　convergence　of　the　algorithm.　In　addition,　a　self-adaptive　hybrid　mechanism　is　developed　to　maintain　a　tradeoff　between　the　global　and-local　optimizer　searching　then　to　efficiently　locate　quality　solution　to　complex　optimization　problem.　The　computational　results　indicate　that　the　global　searching　ability　and　the　convergence　speed　of　this　hybrid　algorithm　are　significantly　improved.　Some　well-known　benchmark　functions　are　utilized　to　test　the　applicability　of　the　proposed　algorithm.