Crossover Context in Page-based Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "G. C. Wilson and M. I. Heywood",
  title =        "Crossover Context in Page-based Linear Genetic
  booktitle =    "IEEE CCECE 2002: IEEE Canadian Conference on
                 Electrical and Computer Engineering",
  year =         "2002",
  editor =       "W. Kinsner and A. Seback and K. Ferens",
  pages =        "809--814",
  volume =       "2",
  month =        "12-15 " # may,
  organisation = "IEEE Canada",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Strategy
                 Learning, learning (artificial intelligence), search
                 problems, San Mateo trail, artificial ants, code
                 sequences, crossover operator, effective search
                 strategies, fitness change, instructions, simple
                 register based memories, strategy learning",
  ISBN =         "0-7803-7515-7",
  ISSN =         "0840-7789",
  URL =          "",
  DOI =          "doi:10.1109/CCECE.2002.1013046",
  size =         "6 pages",
  abstract =     "This work explores strategy learning through genetic
                 programming in artificial ants that navigate the San
                 Mateo trail. We investigate several properties of
                 linearly structured (as opposed to typical tree based)
                 GP including: the significance of simple register based
                 memories, the significance of constraints applied to
                 the crossover operator, and how active the ant are. We
                 also provide a basis for investigating more thoroughly
                 the relation between specific code sequences and
                 fitness by dividing the genome into pages of
                 instructions and introducing an analysis of fitness
                 change and exploration of the trail done by particular
                 parts of a genome. By doing so we are able to present
                 results on how best to find the instructions in an
                 individual's program that contribute positively to the
                 accumulation of effective search strategies.",
  notes =        "best student paper award",

Genetic Programming entries for Garnett Carl Wilson Malcolm Heywood