Large Populations Are Not Always The Best Choice In Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8670

@InProceedings{fuchs:1999:LPANATBCIGP,

• author = "Matthias Fuchs",
• title = "Large Populations Are Not Always The Best Choice In Genetic Programming",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference",
• year = "1999",
• editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben and Max H. Garzon and Vasant Honavar and Mark Jakiela and Robert E. Smith",
• volume = "2",
• pages = "1033--1038",
• address = "Orlando, Florida, USA",
• publisher_address = "San Francisco, CA 94104, USA",
• month = "13-17 " # jul,
• publisher = "Morgan Kaufmann",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "1-55860-611-4",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-410.pdf",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco1999/GP-410.ps",
• size = "6 pages",
• abstract = "In genetic programming a general consensus is that the population should be as large as practically possible or sensible. we examine a batch of problems of combinatory logic, previously successfully tackled with genetic programming, which seem to defy this consensus. Our experimental data gives evidence that smaller populations are competitive or even slightly better. Moreover, hill-climbing appears to exhibit the best performance. While these results are in a way unexpected, theoretical considerations provide a possible explanation in terms of a special constellation rather than a general misconception as to the benefits of large populations or genetic programming as such.",
• notes = "GECCO-99 A joint meeting of the eighth international conference on genetic algorithms (ICGA-99) and the fourth annual genetic programming conference (GP-99)",

}

Genetic Programming entries for Matthias Fuchs

Citations