GDF v2.0, an enhanced version of GDF

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@Article{Tsoulos2007976,
  author =       "Ioannis G. Tsoulos and Dimitris Gavrilis and 
                 Evangelos Dermatas",
  title =        "GDF v2.0, an enhanced version of GDF",
  journal =      "Computer Physics Communications",
  volume =       "177",
  number =       "12",
  pages =        "976--977",
  year =         "2007",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Function approximation, Stochastic methods,
                 Grammatical evolution",
  ISSN =         "0010-4655",
  oai =          "oai:CiteSeerX.psu:10.1.1.541.2453",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.541.2453",
  URL =          "http://users.cs.uoi.gr/~itsoulos/publications/gdf2.pdf",
  DOI =          "DOI:10.1016/j.cpc.2007.08.008",
  URL =          "http://www.sciencedirect.com/science/article/B6TJ5-4PJM9P8-4/2/ee8eb3dbd6401fb5375a6f4034f76feb",
  size =         "15 pages",
  abstract =     "An improved version of the function estimation program
                 GDF is presented. The main enhancements of the new
                 version include: multi-output function estimation,
                 capability of defining custom functions in the grammar
                 and selection of the error function. The new version
                 has been evaluated on a series of classification and
                 regression datasets, that are widely used for the
                 evaluation of such methods. It is compared to two known
                 neural networks and outperforms them in 5 (out of 10)
                 datasets.Program summary Title of program: GDF v2.0
                 Catalogue identifier: ADXC_v2_0 Program summary URL:
                 http://cpc.cs.qub.ac.uk/summaries/ADXC_v2_0.html
                 Program obtainable from: CPC Program Library, Queen's
                 University, Belfast, N. Ireland Licensing provisions:
                 Standard CPC license,
                 http://cpc.cs.qub.ac.uk/licence/licence.html No. of
                 lines in distributed program, including test data,
                 etc.: 98[thin space]147 No. of bytes in distributed
                 program, including test data, etc.: 2[thin
                 space]040[thin space]684 Distribution format: tar.gz
                 Programming language: GNU C++ Computer:The program is
                 designed to be portable in all systems running the GNU
                 C++ compiler Operating system: Linux, Solaris, FreeBSD
                 RAM: 200000 bytes Classification: 4.9 Does the new
                 version supersede the previous version?: Yes Nature of
                 problem: The technique of function estimation tries to
                 discover from a series of input data a functional form
                 that best describes them. This can be performed with
                 the use of parametric models, whose parameters can
                 adapt according to the input data. Solution method:
                 Functional forms are being created by genetic
                 programming which are approximations for the symbolic
                 regression problem. Reasons for new version: The GDF
                 package was extended in order to be more flexible and
                 user customizable than the old package. The user can
                 extend the package by defining his own error functions
                 and he can extend the grammar of the package by adding
                 new functions to the function repertoire. Also, the new
                 version can perform function estimation of multi-output
                 functions and it can be used for classification
                 problems. Summary of revisions: The following features
                 have been added to the package GDF: - Multi-output
                 function approximation. The package can now approximate
                 any function . This feature gives also to the package
                 the capability of performing classification and not
                 only regression. - User defined function can be added
                 to the repertoire of the grammar, extending the
                 regression capabilities of the package. This feature is
                 limited to 3 functions, but easily this number can be
                 increased. - Capability of selecting the error
                 function. The package offers now to the user apart from
                 the mean square error other error functions such as:
                 mean absolute square error, maximum square error. Also,
                 user defined error functions can be added to the set of
                 error functions. - More verbose output. The main
                 program displays more information to the user as well
                 as the default values for the parameters. Also, the
                 package gives to the user the capability to define an
                 output file, where the output of the gdf program for
                 the testing set will be stored after the termination of
                 the process. Additional comments: A technical report
                 describing the revisions, experiments and test runs is
                 packaged with the source code. Running time: Depending
                 on the train data.",
}

Genetic Programming entries for Ioannis G Tsoulos Dimitris Gavrilis Evangelos Dermatas

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