A schema theory analysis of the evolution of size in genetic programming with linear representations

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

@InProceedings{mcphee:2001:EuroGP,
  author =       "Nicholas Freitag McPhee and Riccardo Poli",
  title =        "A schema theory analysis of the evolution of size in
                 genetic programming with linear representations",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and 
                 Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and 
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "108--125",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Schema
                 theory, Linear representations, Bloat, Length
                 distributions, Fitness landscape glitches,
                 One-then-zeros problem",
  ISBN =         "3-540-41899-7",
  URL =          "http://cswww.essex.ac.uk/staff/poli/papers/McPhee-EUROGP2001-ST-Linear-Bloat.pdf",
  URL =          "http://citeseer.ist.psu.edu/502073.html",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=108",
  size =         "18 pages",
  abstract =     "In this paper we use the schema theory presented
                 elsewhere in this volume to better understand the
                 changes in size distribution when using GP with
                 standard crossover and linear structures. Applications
                 of the theory to problems both with and without fitness
                 suggest that standard crossover induces specific biases
                 in the distributions of sizes, with a strong tendency
                 to over sample small structures, and indicate the
                 existence of strong redistribution effects that may be
                 a major force in the early stages of a GP run. We also
                 present two important theoretical results: An exact
                 theory of bloat, and a general theory of how average
                 size changes on flat landscapes with glitches. The
                 latter implies the surprising result that a single
                 program glitch in an otherwise flat fitness landscape
                 is sufficient to drive the average program size of an
                 infinite population, which may have important
                 implications for the control of code growth.",
  notes =        "EuroGP'2001, part of \cite{miller:2001:gp} Update of
                 \cite{McPhee00-22}",
}

Genetic Programming entries for Nicholas Freitag McPhee Riccardo Poli