Controlling the Population Size in Genetic Programming

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

@InProceedings{sbia2002meta033,
  title =        "Controlling the Population Size in Genetic
                 Programming",
  author =       "Eduardo Spinosa and Aurora Pozo",
  year =         "2002",
  identifier =   "sbia2002article033",
  language =     "eng",
  rights =       "Sociedade Brasileira de Computa{\c c}{\~a}o",
  source =       "sbia2002",
  booktitle =    "Advances in Artificial Intelligence: 16th Brazilian
                 Symposium on Artificial Intelligence, SBIA 2002",
  editor =       "G. Bittencourt and G .L. Ramalho",
  volume =       "2507",
  series =       "Lecture Notes in Computer Science",
  pages =        "345--354",
  address =      "Porto de Galinhas, Recife, Brasil",
  month =        "11-14 " # nov,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/3-540-36127-8_33",
  abstract =     "Evolutionary Computation (EC) introduces a new
                 paradigm for solving problems in Artificial
                 Intelligence, representing solution candidates as
                 individuals and evolving them based on Darwin's Theory
                 of Natural Selection. Genetic Algorithms (GA) and
                 Genetic Programming (GP), two important EC techniques,
                 have been successfully applied both in theoretical
                 scenarios and practical situations. This work discusses
                 an issue of great relevance and impact on this type of
                 algorithm: the automatic adjustment of the parameters
                 that control the search process. Based on a recent
                 research, a method that controls the population size in
                 a GA is adapted and implemented in GP. A series of
                 classic experiments has been performed before and after
                 the modifications, showing that this method can improve
                 the algorithms' robustness and reliability. The data
                 allow a discussion about the method and the importance
                 of the adaptation of parameters in EC algorithms.",
}

Genetic Programming entries for Eduardo J Spinosa Aurora Trinidad Ramirez Pozo

Citations