@article{scholars3149,
       publisher = {World Scientific Publishing Co. Pte Ltd},
         journal = {International Journal of Modeling, Simulation, and Scientific Computing},
            year = {2012},
           title = {A novel hybrid genetic algorithms and pattern search techniques for industrial production planning},
             doi = {10.1142/S1793962312500201},
          number = {4},
          volume = {3},
            note = {cited By 37},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869751748&doi=10.1142\%2fS1793962312500201&partnerID=40&md5=a7aafa9517923b080ed43581e4f8778b},
        keywords = {Decision making; Genetic algorithms; Global optimization; Planning; Problem solving; Production control; Soft computing, Computational intelligent techniques; Degree of satisfaction; Global optimization method; Hybrid genetic algorithms; Industrial production; Pattern search; Production Planning; Traditional approaches, Optimization},
        abstract = {Soft computing has attracted many research scientists, decision makers and practicing researchers in recent years as powerful computational intelligent techniques, for solving unlimited number of complex real-world problems particularly related to research area of optimization. Under the uncertain and turbulence environment, classical and traditional approaches are unable to obtain a complete solution with satisfaction for the real-world problems on optimization. Therefore, new global optimization methods are required to handle these issues seriously. One such method is hybrid Genetic algorithms and Pattern search, a generic, flexible, robust, and versatile framework for solving complex problems of global optimization and search in real-world applications. {\^A}{\copyright} 2012 World Scientific Publishing Company.},
          author = {Vasant, P.},
            issn = {17939623}
}