Towards Practical Autoconstructive Evolution: Self-Evolution of Problem-Solving Genetic Programming Systems

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

@InCollection{spector:2010:GPTP,
  author =       "Lee Spector",
  title =        "Towards Practical Autoconstructive Evolution:
                 Self-Evolution of Problem-Solving Genetic Programming
                 Systems",
  booktitle =    "Genetic Programming Theory and Practice VIII",
  year =         "2010",
  editor =       "Rick Riolo and Trent McConaghy and 
                 Ekaterina Vladislavleva",
  series =       "Genetic and Evolutionary Computation",
  volume =       "8",
  address =      "Ann Arbor, USA",
  month =        "20-22 " # may,
  publisher =    "Springer",
  chapter =      "2",
  pages =        "17--33",
  keywords =     "genetic algorithms, genetic programming, meta-genetic
                 programming, autoconstructive evolution, Push, PushGP,
                 Pushpop, AutoPush",
  isbn13 =       "978-1-4419-7746-5",
  URL =          "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.472.6583",
  URL =          "http://faculty.hampshire.edu/lspector/pubs/spector-gptp10-preprint.pdf",
  size =         "18 pages",
  abstract =     "Most genetic programming systems use hard-coded
                 genetic operators that are applied according to
                 user-specified parameters. Because it is unlikely that
                 the provided operators or the default parameters will
                 be ideal for all problems or all program
                 representations, practitioners often devote
                 considerable energy to experimentation with
                 alternatives. Attempts to bring choices about operators
                 and parameters under evolutionary control, through
                 self-adaptative algorithms or meta-genetic programming,
                 have been explored in the literature and have produced
                 interesting results. However, no systems based on such
                 principles have yet been demonstrated to have greater
                 practical problem-solving power than the more-standard
                 alternatives. This chapter explores the prospects for
                 extending the practical power of genetic programming
                 through the refinement of an approach called
                 autoconstructive evolution, in which the algorithms
                 used for the reproduction and variation of evolving
                 programs are encoded in the programs themselves, and
                 are thereby subject to variation and evolution in
                 tandem with their problem-solving components. We
                 present the motivation for the autoconstructive
                 evolution approach, show how it can be instantiated
                 using the Push programming language, summarise previous
                 results with the Pushpop system, outline the more
                 recent AutoPush system, and chart a course for future
                 work focused on the production of practical systems
                 that can solve hard problems.",
  notes =        "part of \cite{Riolo:2010:GPTP}",
}

Genetic Programming entries for Lee Spector

Citations