An Analysis of the MAX Problem in Genetic Programming

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

@InProceedings{langdon:1997:MAX,
  author =       "W. B. Langdon and R. Poli",
  title =        "An Analysis of the {MAX} Problem in Genetic
                 Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and 
                 David B. Fogel and Max Garzon and Hitoshi Iba and 
                 Rick L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  pages =        "222--230",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.max_gp97.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.max_gp97.ps",
  size =         "9 pages",
  abstract =     "We present a detailed analysis of the evolution of GP
                 populations using the problem of finding a program
                 which returns the maximum possible value for a given
                 terminal and function set and a depth limit on the
                 program tree (known as the MAX problem). We confirm the
                 basic message of \cite{Gathercole:1996:aicrtd} that
                 crossover together with program size restrictions can
                 be responsible for premature convergence to a
                 sub-optimal solution. We show that this can happen even
                 when the population retains a high level of variety and
                 show that in many cases evolution from the sub-optimal
                 solution to the solution is possible if sufficient time
                 is allowed. In both cases theoretical models are
                 presented and compared with actual runs. Price's
                 Covariance and Selection Theorem is experimentally
                 tested on GP populations. It is shown to hold only in
                 some cases, in others program size restrictions cause
                 important deviation from its predictions.",
  notes =        "GP-97 Considerable update of \cite{Langdon97}",
}

Genetic Programming entries for William B Langdon Riccardo Poli

Citations