A Hyper-heuristic approach towards mitigating Premature Convergence caused by the objective fitness function in GP

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Ragalo:2014:ICISDA,
  author =       "Anisa Ragalo and Nelishia Pillay",
  booktitle =    "14th International Conference on Intelligent Systems
                 Design and Applications",
  title =        "A Hyper-heuristic approach towards mitigating
                 Premature Convergence caused by the objective fitness
                 function in GP",
  year =         "2014",
  pages =        "68--75",
  abstract =     "This manuscript proposes a hyper-heuristic approach
                 towards mitigating Premature Convergence caused by
                 objective fitness in Genetic Programming (GP). The
                 objective fitness function used in standard GP has the
                 potential to profoundly exacerbate Premature
                 Convergence in the algorithm. Accordingly several
                 alternative fitness measures have been proposed in GP
                 literature. These alternative fitness measures replace
                 the objective function, with the specific aim of
                 mitigating this type of Premature Convergence. However
                 each alternative fitness measure is found to have its
                 own intrinsic limitations. To this end the proposed
                 approach automates the selection of distinct fitness
                 measures during the progression of GP. The power of
                 this methodology lies in the ability to compensate for
                 the weaknesses of each fitness measure by automating
                 the selection of the best alternative fitness measure.
                 Our hyper-heuristic approach is found to achieve
                 generality in the alleviation of Premature Convergence
                 caused by objective fitness. Vitally the approach is
                 unprecedented and highlights a new paradigm in the
                 design of GP systems.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ISDA.2014.7066272",
  ISSN =         "2164-7143",
  month =        nov,
  notes =        "School of Mathematics, Statistics and Computer
                 Science, University of KwaZulu-Natal, Pietermaritzburg,
                 South Africa

                 Also known as \cite{7066272}",
}

Genetic Programming entries for Anisa Waganda Ragalo Nelishia Pillay

Citations