Evolutionary tree genetic programming

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

@InProceedings{1068312,
  author =       "Jan Antolik and William H. Hsu",
  title =        "Evolutionary tree genetic programming",
  booktitle =    "{GECCO 2005}: Proceedings of the 2005 conference on
                 Genetic and evolutionary computation",
  year =         "2005",
  editor =       "Hans-Georg Beyer and Una-May O'Reilly and 
                 Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and 
                 Eric W. Bonabeau and Erick Cantu-Paz and 
                 Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and 
                 Edwin D. {de Jong} and Hod Lipson and Xavier Llora and 
                 Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and 
                 Terence Soule and Andy M. Tyrrell and 
                 Jean-Paul Watson and Eckart Zitzler",
  volume =       "2",
  ISBN =         "1-59593-010-8",
  pages =        "1789--1790",
  address =      "Washington DC, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1789.pdf",
  DOI =          "doi:10.1145/1068009.1068312",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "25-29 " # jun,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Poster",
  abstract =     "We introduce a clustering-based method of
                 subpopulation management in genetic programming (GP)
                 called Evolutionary Tree Genetic Programming (ETGP).
                 The biological motivation behind this work is the
                 observation that the natural evolution follows a
                 tree-like phylogenetic pattern. Our goal is to simulate
                 similar behavior in artificial evolutionary systems
                 such as GP. To test our model we use three common GP
                 benchmarks: the Ant Algorithm, 11-Multiplexer, and
                 Parity problems.The performance of the ETGP system is
                 empirically compared to those of the GP system. Code
                 size and variance are consistently reduced by a small
                 but statistically significant percentage, resulting in
                 a slight speedup in the Ant and 11-Multiplexer
                 problems, while the same comparisons on the Parity
                 problem are inconclusive.",
  notes =        "GECCO-2005 A joint meeting of the fourteenth
                 international conference on genetic algorithms
                 (ICGA-2005) and the tenth annual genetic programming
                 conference (GP-2005).

                 ACM Order Number 910052",
}

Genetic Programming entries for Jan Antolik William H Hsu

Citations