Single Parent Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8098

@InProceedings{ashlock:2005:CECw,

• author = "Wendy Ashlock and Dan Ashlock",
• title = "Single Parent Genetic Programming",
• booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation",
• 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 = "1172--1179",
• 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",
• DOI = "doi:10.1109/CEC.2005.1554823",
• size = "8 pages",
• abstract = "The most controversial part of genetic programming is its highly disruptive and potentially innovative subtree crossover operator. The clearest problem with the crossover operator is its potential to induce defensive metaselection for large parse trees, a process usually termed 'bloat'. Single parent genetic programming is a form of genetic programming in which bloat is reduced by doing subtree crossover with a fixed population of ancestor trees. Analysis of mean tree size growth demonstrates that this fixed and limited set of crossover partners provides implicit, automatic control on tree size in the evolving population, reducing the need for additionally disruptive trimming of large trees. The choice of ancestor trees can also incorporate expert knowledge into the genetic programming system. The system is tested on four problems: plus-one-recall-store (PORS), odd parity, plus-times-half (PTH) and a bioinformatic model fitting problem (NIPs). The effectiveness of the technique varies with the problem and choice of ancestor set. At the extremes, improvements in time to solution in excess of 4700-fold were observed for the PORS problem, and no significant improvements for the PTH problem were observed.",
• notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.",

}

Genetic Programming entries for Wendy Ashlock Daniel Ashlock

Citations