Regressor Survival Rate Estimation for Enhanced Crossover Configuration

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Alina Patelli and Lavinia Ferariu",
  title =        "Regressor Survival Rate Estimation for Enhanced
                 Crossover Configuration",
  year =         "2011",
  booktitle =    "10th International Conference on Adaptive and Natural
                 Computing Algorithms, ICANNGA 2011",
  editor =       "Andrej Dobnikar and Uros Lotric and Branko Ster",
  series =       "Lecture Notes in Computer Science",
  volume =       "6593",
  pages =        "290--299",
  address =      "Ljubljana, Slovenia",
  month =        "14-16 " # apr,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, fuzzy
                 control, schema theory, nonlinear systems
                 identification multiobjective optimisation",
  isbn13 =       "978-3-642-20281-0",
  DOI =          "doi:10.1007/978-3-642-20282-7_30",
  size =         "10 pages",
  abstract =     "In the framework of nonlinear systems identification
                 by means of multiobjective genetic programming, the
                 paper introduces a customised crossover operator,
                 guided by fuzzy controlled regressor encapsulation. The
                 approach is aimed at achieving a balance between
                 exploration and exploitation by protecting well adapted
                 subtrees from division during recombination. To reveal
                 the benefits of the suggested genetic operator, the
                 authors introduce a novel mathematical formalism which
                 extends the Schema Theory for cut point crossover
                 operating on trees encoding regressor based models.
                 This general framework is afterwards used for
                 monitoring the survival rates of fit encapsulated
                 structural blocks. Other contributions are proposed in
                 answer to the specific requirements of the
                 identification problem, such as a customized tree
                 building mechanism, enhanced elite processing and the
                 hybridisation with a local optimisation procedure. The
                 practical potential of the suggested algorithm is
                 demonstrated in the context of an industrial
                 application involving the identification of a
                 subsection within the sugar factory of Lublin,
  note =         "Revised selected papers. ICANNGA 2011",

Genetic Programming entries for Alina Patelli Lavinia Ferariu