A Schema Theory Analysis of Mutation Size Biases in Genetic Programming with Linear Representations

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

@InProceedings{mcphee:2001:astamsbgplr,

• author = "Nicholas Freitag McPhee and Riccardo Poli and Jonathan E. Rowe",
• title = "A Schema Theory Analysis of Mutation Size Biases in Genetic Programming with Linear Representations",
• booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001",
• year = "2001",
• pages = "1078--1085",
• address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "27-30 " # may,
• organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, schema theory, mutation, linear representation, size bias",
• ISBN = "0-7803-6658-1",
• URL = "http://cswww.essex.ac.uk/staff/poli/papers/McPhee-CEC2001.pdf",
• URL = "http://cswww.essex.ac.uk/staff/poli/papers/postscript/McPhee-CEC2001.ps.gz",
• URL = "http://citeseer.ist.psu.edu/502355.html",
• URL = "http://citeseer.ist.psu.edu/501380.html",
• DOI = "doi:10.1109/CEC.2001.934311",
• abstract = "Understanding operator bias in evolutionary computation is important because it is possible for the operator's biases to work against the intended biases induced by the fitness function. In recent work we showed how developments in GP schema theory can be used to better understand the biases induced by the standard subtree crossover when genetic programming is applied to variable length linear structures. We use the schema theory to better understand the biases induced on linear structures by two common GP subtree mutation operators: FULL and GROW mutation. In both cases we find that the operators do have quite specific biases and typically strongly oversample shorter strings.",
• notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE.

  IEEE Catalog Number = 01TH8546C,

  Library of Congress Number = .

  linear (unary) tree schemata. flat fitness landscape. biases of full mutation, grow mutation,

  No fitness. Full(unary) average length = 2*D-1. Limiting size distribution: 0 for size < D, flat region size < 2D, rapid falling size>=2D. Similar to subtree crossover. Grow(unary) discrete gamma distribution (cf. \cite{Rowe01} ) cf subtree crossover.

  {"}ones then zeros{"} unary problem. Subtree crossover bloat (at least to 75 generations). full no bloat, actually as with no fitness, {"}artifact of this particular problem{"}. Grow similar to no fitness.",

}

Genetic Programming entries for Nicholas Freitag McPhee Riccardo Poli Jonathan E Rowe

Citations