An Analysis of Koza's Computational Effort Statistic for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{christensen:2002:EuroGP,

• title = "An Analysis of {Koza}'s Computational Effort Statistic for Genetic Programming",
• author = "Steffen Christensen and Franz Oppacher",
• editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
• booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002",
• year = "2002",
• volume = "2278",
• series = "LNCS",
• pages = "182--191",
• publisher = "Springer-Verlag",
• address = "Kinsale, Ireland",
• publisher_address = "Berlin",
• month = "3-5 " # apr,
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-43378-3",
• DOI = "doi:10.1007/3-540-45984-7_18",
• abstract = "As research into the theory of genetic programming progresses, more effort is being placed on systematically comparing results to give an indication of the effectiveness of sundry modifications to traditional GP. The statistic that is commonly used to report the amount of computational effort to solve a particular problem with 99percent probability is Koza's I(M, i, z) statistic. This paper analyzes this measure from a statistical perspective. In particular, Koza's I tends to underestimate the true computational effort, by 25percent or more for commonly used GP parameters and run sizes. The magnitude of this underestimate is nonlinearly decreasing with increasing run count, leading to the possibility that published results based on few runs may in fact be unmatchable when replicated at higher resolution. Additional analysis shows that this statistic also under reports the generation at which optimal results are achieved.",
• notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",

}

Genetic Programming entries for Steffen Christensen Franz Oppacher

Citations