A Dispersion Operator for Geometric Semantic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Oliveira:2016:GECCO,
  author =       "Luiz Otavio V. B. Oliveira and 
                 Fernando E. B. Otero and Gisele Lobo Pappa",
  title =        "A Dispersion Operator for Geometric Semantic Genetic
                 Programming",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "773--780",
  note =         "Best paper",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908923",
  abstract =     "Recent advances in geometric semantic genetic
                 programming (GSGP) have shown that the results obtained
                 by these methods can outperform those obtained by
                 classical genetic programming algorithms, in particular
                 in the context of symbolic regression. However, there
                 are still many open issues on how to improve their
                 search mechanism. One of these issues is how to get
                 around the fact that the GSGP crossover operator cannot
                 generate solutions that are placed outside the convex
                 hull formed by the individuals of the current
                 population. Although the mutation operator alleviates
                 this problem, we cannot guarantee it will find
                 promising regions of the search space within feasible
                 computational time. In this direction, this paper
                 proposes a new geometric dispersion operator that uses
                 multiplicative factors to move individuals to less
                 dense areas of the search space around the target
                 solution before applying semantic genetic operators.
                 Experiments in sixteen datasets show that the results
                 obtained by the proposed operator are statistically
                 significantly better than those produced by GSGP and
                 that the operator does indeed spread the solutions
                 around the target solution.",
  notes =        "UFMG, University of Kent

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",
}

Genetic Programming entries for Luiz Otavio Vilas Boas Oliveira Fernando Esteban Barril Otero Gisele L Pappa

Citations