Simple Implementation of Genetic Programming by Column Tables

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

@InProceedings{Kvasnieka:1997:WSC2,
  author =       "Vladimir Kvasnieka and Jioi Pospichal",
  title =        "Simple Implementation of Genetic Programming by Column
                 Tables",
  booktitle =    "Soft Computing in Engineering Design and
                 Manufacturing",
  year =         "1997",
  editor =       "Pravir K. Chawdhry and Rajkumar Roy and Raj K. Pant",
  pages =        "48--56",
  publisher_address = "Godalming, GU7 3DJ, UK",
  month =        "23-27 " # jun,
  publisher =    "Springer-Verlag London",
  keywords =     "genetic algorithms, genetic programming, directed
                 acyclic graphs, symbolic regression, DAG",
  ISBN =         "3-540-76214-0",
  URL =          "http://www.springer.com/engineering/mechanical+eng/book/978-3-540-76214-0?cm_mmc=Google-_-Book%20Search-_-Springer-_-0",
  DOI =          "doi:10.1007/978-1-4471-0427-8_6",
  abstract =     "Simple implementation of genetic programming by making
                 use of the column tables is discussed. Implementations
                 of Koza's genetic programming in compiled languages are
                 usually not most efficient when crossover is applied.
                 If chromosomes are directed acyclic graphs, more
                 efficient than rooted trees both in memory requirement
                 as well as in evaluation time of chromosome, then
                 crossover requires traversing the data structures and
                 their preliminary analysis. Column tables inherently
                 code directed acyclic graphs, the implementation of
                 crossover is simple and needs neither traversing nor
                 checking of integrity of resulting data structures and
                 should be therefore more efficient. Stochastic
                 transformation operation mutation is also easily
                 defined. Column tables can represent graphs with
                 several output nodes and may be used e.g. for
                 optimization of feed-forward neural networks. Simple
                 illustrative examples of symbolic regression based on
                 the column tables are presented.",
  notes =        "WSC2 Second On-line World Conference on Soft Computing
                 in Engineering Design and
                 Manufacturing

                 wsc2/ind_paper/p_kvasni.html URL broken 2005",
}

Genetic Programming entries for Vladimir Kvasnicka Jiri Pospichal

Citations