Modification Point Depth and Genome Growth in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{luke:2003:ECJ,
  author =       "Sean Luke",
  title =        "Modification Point Depth and Genome Growth in Genetic
                 Programming",
  year =         "2003",
  journal =      "Evolutionary Computation",
  volume =       "11",
  number =       "1",
  pages =        "67--106",
  month =        "Spring",
  keywords =     "genetic algorithms, genetic programming, Introns,
                 Inviable Code, Code Bloat, Crossover Point",
  DOI =          "doi:10.1162/106365603321829014",
  abstract =     "The evolutionary computation community has shown
                 increasing interest in arbitrary-length
                 representations, particularly in the field of genetic
                 programming. A serious stumbling block to the
                 scalability of such representations has been bloat:
                 uncontrolled genome growth during an evolutionary run.
                 Bloat appears across the evolutionary computation
                 spectrum, but genetic programming has given it by far
                 the most attention. Most genetic programming models
                 explain this phenomenon as a result of the growth of
                 introns, areas in an individual which serve no
                 functional purpose. This paper presents evidence which
                 directly contradicts intron theories as applied to
                 tree-based genetic programming. The paper then uses
                 data drawn from this evidence to propose a new model of
                 genome growth. In this model, bloat in genetic
                 programming is a function of the mean depth of the
                 modification (crossover or mutation) point. Points far
                 from the root are correspondingly less likely to hurt
                 the child's survivability in the next generation. The
                 modication point is in turn strongly correlated to
                 average parent tree size and to removed subtree size,
                 both of which are directly linked to the size of the
                 resulting child.",
}

Genetic Programming entries for Sean Luke

Citations