Linear Genetic Programming using a compressed genotype representation

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Johan Parent and Ann Nowe and Kris Steenhaut and 
                 Anne Defaweux",
  title =        "Linear Genetic Programming using a compressed genotype
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and 
                 Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Tan Kay Chen and 
                 Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and 
                 Jennifier Willies and Juan J. Merelo Guervos and 
                 Eugene Eberbach and Bob McKay and Alastair Channon and 
                 Ashutosh Tiwari and L. Gwenn Volkert and 
                 Dan Ashlock and Marc Schoenauer",
  volume =       "2",
  pages =        "1164--1171",
  address =      "Edinburgh, UK",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "2-5 " # sep,
  organisation = "IEEE Computational Intelligence Society, Institution
                 of Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9363-5",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2005.1554822",
  abstract =     "This paper presents a modularisation strategy for
                 linear genetic programming (GP) based on a substring
                 compression/substitution scheme. The purpose of this
                 substitution scheme is to protect building blocks and
                 is in other words a form of learning linkage. The
                 compression of the genotype provides both a protection
                 mechanism and a form of genetic code reuse. This paper
                 presents results for synthetic genetic algorithm (GA)
                 reference problems like SEQ and OneMax as well as
                 several standard GP problems. These include a real
                 world application of GP to data compression. Results
                 show that despite the fact that the compression
                 substrings assumes a tight linkage between alleles,
                 this approach improves the search process.",
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.",

Genetic Programming entries for Johan Parent Ann Nowe Kris Steenhaut Anne Defaweux