Evolution of Genetic Code on a Hard Problem

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

@InProceedings{keller:2001:gecco,

• title = "Evolution of Genetic Code on a Hard Problem",
• author = "Robert E. Keller and Wolfgang Banzhaf",
• pages = "50--56",
• year = "2001",
• publisher = "Morgan Kaufmann",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)",
• editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke",
• address = "San Francisco, California, USA",
• publisher_address = "San Francisco, CA 94104, USA",
• month = "7-11 " # jul,
• keywords = "genetic algorithms, genetic programming, genetic code, real-world problem, noise filtering, developmental genetic programming, genotype-phenotype mapping, self-adaptation",
• ISBN = "1-55860-774-9",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf",
• size = "7 pages",
• abstract = "In most Genetic Programming (GP) approaches, the space of genotypes, that is the searchspace, is identical to the space of phenotypes, that is the solution space. Developmental approaches, like Developmental Genetic Programming (DGP), distinguish between genotypes and phenotypes and use a genotype-phenotype mapping prior to fitness evaluation of a phenotype. To perform this mapping, DGP uses a genetic code, that is, a mapping from genotype components to phenotype components. The genotype-phenotype mapping is critical for the performance of the underlying search process which is why adapting the mapping to a given problem is of interest. Previous work shows, on an easy synthetic problem, the feasibility of code evolution to the effect of a problem-specific self-adaptation of the mapping. The present empirical work delivers a demonstration of this effect on a hard synthetic problem, showing the real-world potential of code evolution which increases the occurrence of relevant phenotypic components and reduces the occurrence of components that represent noise.",
• notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}",

}

Genetic Programming entries for Robert E Keller Wolfgang Banzhaf

Citations