PolyGP: A Polymorphic Genetic Programming System in Haskell

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{yu:1998:PolyGP,

• author = "Tina Yu and Chris Clack",
• title = "PolyGP: A Polymorphic Genetic Programming System in Haskell",
• booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference",
• year = "1998",
• editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo",
• pages = "416--421",
• address = "University of Wisconsin, Madison, Wisconsin, USA",
• publisher_address = "San Francisco, CA, USA",
• month = "22-25 " # jul,
• publisher = "Morgan Kaufmann",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "1-55860-548-7",
• URL = "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/pgp.new.pdf",
• size = "6 pages",
• abstract = "In general, the machine learning process can be accelerated through the use of additional knowledge about the problem solution. For example, monomorphic typed Genetic Programming (GP) uses type information to reduce the search space and improve performance. Unfortunately, monomorphic typed GP also loses the generality of untyped GP: the generated programs are only suitable for inputs with the specified type. Polymorphic typed GP improves over monomorphic and untyped GP by allowing the type information to be expressed in a more generic manner, and yet still imposes constraints on the search space. This paper describes a polymorphic GP system which can generate polymorphic programs: programs which take inputs of more than one type and produce outputs of more than one type.",
• notes = "GP-98 slides http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/poly.slide.pdf broken Aug 2016",

}

Genetic Programming entries for Tina Yu Christopher D Clack

Citations