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

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@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",
  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