A Gene Expression Programming Framework for Evolutionary Design of Metaheuristic Algorithms

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@InProceedings{Rahati:2016:CEC,
  author =       "Amin Rahati and Hojjat Rakhshani",
  title =        "A Gene Expression Programming Framework for
                 Evolutionary Design of Metaheuristic Algorithms",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "1445--1452",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Optimization; Metaheuristic
                 Algorithms",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7743960",
  abstract =     "Metaheuristic algorithms have successfully tackled
                 many difficult and ill-conditioned optimization
                 problems. Nevertheless, performance of these methods is
                 subjected to the complexity and fitness landscape of
                 the problem at hand. Accordingly, designing
                 metaheuristic algorithms that work well on a variety of
                 optimization problems is not a trivial task. In this
                 study, we introduce a novel framework for improving
                 generalization capability of the metaheuristic
                 algorithms based on the notion of gene expression
                 programming (GEP). The proposed framework introduces a
                 modified GEP (MGEP) in order to adaptively design
                 search operators of a metaheuristic algorithm. During
                 evolution process, a multi-criteria procedure
                 determines the search operators that are preferable and
                 can obtain high accuracy results. Performance of the
                 proposed approach is empirically evaluated on CEC 2013
                 test suite. The obtained results confirm that the
                 evolved metaheuristic algorithms by this framework
                 perform similarly to or better than the standard
                 versions.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Amin Rahati Hojjat Rakhshani

Citations