Development and Evaluation of an Open-Ended Computational Evolution System for the Creation of Digital Organisms with Complex Genetic Architecture

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

  author =       "Anna L. Tyler and Bill C. White and 
                 Casey S. Greene and Peter C. Andrews and Richard Cowper-Sal-lari and 
                 Jason H. Moore",
  title =        "Development and Evaluation of an Open-Ended
                 Computational Evolution System for the Creation of
                 Digital Organisms with Complex Genetic Architecture",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "2907--2912",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P105.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983308",
  abstract =     "Epistasis, or gene-gene interaction, is a ubiquitous
                 phenomenon that is inadequately addressed in human
                 genetic studies. There are few tools that can
                 accurately identify high order epistatic interactions,
                 and there is a lack of general understanding as to how
                 epistatic interactions fit into genetic architecture.
                 Here we approach both problems through the lens of
                 genetic programming (GP). It has recently been proposed
                 that increasing open-endedness of GP will result in
                 more complex solutions that better acknowledge the
                 complexity of human genetic datasets. Moreover, the
                 solutions evolved in open-ended GP can serve as model
                 organisms in which to study general effects of
                 epistasis on phenotype. Here we introduce a prototype
                 computational evolution system that implements an
                 open-ended GP and generates organisms that display
                 epistatic interactions. These interactions are
                 significantly more prevalent and have a greater effect
                 on fitness than epistatic interactions in organisms
                 generated in the absence of selection.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "epistasis in open-ended GP by replacing two
                 instructions (genes) with NOP.

                 Fig 2. shows first instruction liable to have epistasis
                 with many other instructions in (linear)

                 Nested toroidal grids (8 neighbours) of populations:
                 mutation probability (1x1), mutation operators (3x3),
                 solution operations (9x9), Avida like code (18x18).
                 Regression 2x+4. C++. Fitness of each operator given by
                 averaging over 3x3 organisms it has evolved. 100 gens
                 (5 secs) bloats average 8-> 20 instructions per

                 P2911 {"}Alife and GP poised to help us take great
                 strides in our understanding of human genetic and the
                 genetic of human common disease. cites

                 CEC 2009 - A joint meeting of the IEEE, the EPS and the
                 IET. IEEE Catalog Number: CFP09ICE-CDR",

Genetic Programming entries for Anna L Tyler Bill C White Casey S Greene Peter C Andrews Richard Cowper-Sal-lari Jason H Moore