Combinatorial Optimization by Gene Expression Programming: Inversion Revisited

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@InProceedings{ferreira:2002:ASIA,
  author =       "Candida Ferreira",
  title =        "Combinatorial Optimization by Gene Expression
                 Programming: Inversion Revisited",
  booktitle =    "Proceedings of the Argentine Symposium on Artificial
                 Intelligence",
  year =         "2002",
  editor =       "J. M. Santos and A. Zapico",
  pages =        "160--174",
  address =      "Santa Fe, Argentina",
  keywords =     "genetic algorithms, genetic programming, GEP",
  URL =          "http://www.gene-expression-programming.com/webpapers/ferreira-ASAI02.pdf",
  size =         "9 pages",
  abstract =     "Combinatorial optimisation problems require
                 combinatorial-specific search operators so that
                 populations of candidate solutions can evolve
                 efficiently. Indeed, several researchers created
                 modifications to the basic genetic operators of
                 mutation and recombination in order to create high
                 performing combinatorial-specific operators. However,
                 it is not known which operators perform better as no
                 systematic comparisons have been done. In this work, a
                 new algorithm that explores a new chromosomal
                 organisation based on multigene families is used. This
                 new organization together with several
                 combinatorial-specific search operators, namely,
                 inversion, gene and sequence deletion/insertion, and
                 restricted and generalised permutation, allow the
                 algorithm to perform with high efficiency. The
                 performance of the new algorithm is empirically
                 compared on the 13- and 19-cities tour travelling
                 salesperson problem, showing that the long abandoned
                 inversion operator is by far the most efficient of the
                 combinatorial operators. The efficiency and
                 potentialities of the new algorithm are further
                 demonstrated by solving a simple task assignment
                 problem.",
  notes =        "ASAI02
                 http://www.dc.uba.ar/people/profesores/santos/asai2002.html",
}

Genetic Programming entries for Candida Ferreira

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