Interpreting a Genotype-Phenotype Map with Rich Representations in XMLGE

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  title =        "Interpreting a Genotype-Phenotype Map with Rich
                 Representations in XMLGE",
  author =       "Saoirse Amarteifio",
  school =       "University of Limerick",
  year =         "2005",
  type =         "Master of Science in Computer Science",
  address =      "University of Limerick, Ireland",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, xml",
  URL =          "",
  size =         "177 pages",
  abstract =     "A novel XML implementation of Grammatical Evolution is
                 developed. This has a number of interesting features
                 such as the use of XSLT for genetic operators and the
                 use of reflection to build an object tree from an XML
                 expression tree. This framework is designed to be used
                 for remote or local evaluation of evolved program
                 structures and provides a number of abstraction layers
                 for program evaluation and evolution.

                 A dynamical swarm system is evolved as a special-case
                 function induction problem to illustrate the
                 application of XMLGE. Particle behaviours are evolved
                 to optimise colony performance.

                 A dual process evolutionary algorithm based on the
                 immune system using rich representations is developed.
                 A dual process feature detection and feature
                 integration model is described and the performance
                 shown on benchmark GP problems. An adaptive feature
                 detection method uses coevolving XPath antibodies to
                 take selective interest in primary structures. Grammars
                 are used to generate reciprocal binding structures
                 (antibodies) given any primary domain grammar.

                 A codon compression algorithm is developed which shows
                 performance improvements on symbolic regression and
                 multiplexer problems. The algorithm is based on
                 questions about the information content of a genome.
                 This also exploits information from the rich
                 representation of XMLGE.",
  language =     "en",

Genetic Programming entries for Saoirse Amarteifio