Problem Difficulty and Code Growth in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{gustafson:2004:GPEM,

• author = "Steven Gustafson and Aniko Ekart and Edmund Burke and Graham Kendall",
• title = "Problem Difficulty and Code Growth in Genetic Programming",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2004",
• volume = "5",
• number = "3",
• pages = "271--290",
• month = sep,
• keywords = "genetic algorithms, genetic programming, population diversity, code growth, problem difficulty",
• ISSN = "1389-2576",
• URL = "http://www.gustafsonresearch.com/research/publications/gustafson-gpem2004.pdf",
• DOI = "doi:10.1023/B:GENP.0000030194.98244.e3",
• size = "20 pages",
• abstract = "the relationship between code growth and problem difficulty in genetic programming. The symbolic regression problem domain is used to investigate this relationship using two different types of increased instance difficulty. Results are supported by a simplified model of genetic programming and show that increased difficulty induces higher selection pressure and less genetic diversity, which both contribute toward an increased rate of code growth.",
• notes = "Article ID: 5272970",

}

Genetic Programming entries for Steven M Gustafson Aniko Ekart Edmund Burke Graham Kendall

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