Improving the Scalability of Generative Representations

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Gregory S. Hornby",
  title =        "Improving the Scalability of Generative
  booktitle =    "Genetic Programming Theory and Practice {V}",
  year =         "2007",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  series =       "Genetic and Evolutionary Computation",
  chapter =      "8",
  pages =        "127--144",
  address =      "Ann Arbor",
  month =        "17-19" # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-387-76308-8",
  DOI =          "doi:10.1007/978-0-387-76308-8_8",
  size =         "17 pages",
  abstract =     "With the recent examples of the human-competitiveness
                 of evolutionary design systems, it is not of interest
                 to scale them up to produce more sophisticated designs.
                 Here we argue that for computer-automated design
                 systems to scale to producing more sophisticated
                 results they must be able to produce designs with
                 greater structure and organisation. By structure and
                 organization we mean the characteristics of modularity,
                 reuse and hierarchy (MR&H), characteristics that
                 are found both in man-made and natural designs. We
                 claim that these characteristics are enabled by
                 implementing the attributes of combination,
                 control-flow and abstraction in the representation, and
                 define metrics for measuring MR&H and define two
                 measures of overall structure and organisation by
                 combining the measures of MR&H. To demonstrate the
                 merit of our complexity measures, we use an
                 evolutionary algorithm to evolve solutions to different
                 sizes for a table design problem, and compare the
                 structure and organisation scores of the best tables
                 against existing complexity measures. We find that our
                 measures better correlate with the complexity of good
                 designs than do others, which supports our claim that
                 MR&H are important components of complexity. We
                 also compare evolution using five representations with
                 different combinations of MR&H, and find that the
                 best designs are achieved when all three of these
                 attributes are present. The results of this second set
                 of experiments demonstrate that implementing
                 representations with MR&H can greatly improve
                 search performance.",
  notes =        "part of \cite{Riolo:2007:GPTP} Published 2008",
  affiliation =  "NASA Ames Research Center U. C. Santa Cruz, Mail Stop
                 269-3 Moffett Field CA 94035",

Genetic Programming entries for Gregory S Hornby