Using Genetic Programming to Obtain a Closed-Form Approximation to a Recursive Function

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

  author =       "Evan Kirshenbaum and Henri J. Suermondt",
  title =        "Using Genetic Programming to Obtain a Closed-Form
                 Approximation to a Recursive Function",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2004,
                 Part II",
  year =         "2004",
  editor =       "Kalyanmoy Deb and Riccardo Poli and 
                 Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and 
                 Paul Darwen and Dipankar Dasgupta and Dario Floreano and 
                 James Foster and Mark Harman and Owen Holland and 
                 Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and 
                 Dirk Thierens and Andy Tyrrell",
  series =       "Lecture Notes in Computer Science",
  pages =        "543--556",
  address =      "Seattle, WA, USA",
  publisher_address = "Heidelberg",
  month =        "26-30 " # jun,
  organisation = "ISGEC",
  publisher =    "Springer-Verlag",
  volume =       "3103",
  ISBN =         "3-540-22343-6",
  ISSN =         "0302-9743",
  DOI =          "doi:10.1007/b98645",
  size =         "14 pages",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We demonstrate a fully automated method for obtaining
                 a closed form approximation of a recursive function.
                 This method resulted from a real world problem in which
                 we had a detector that monitors a time series and where
                 we needed an indication of the total number of false
                 positives expected over a fixed amount of time. The
                 problem, because of the constraints on the available
                 measurements on the detector, was formulated as a
                 recursion, and conventional methods for solving the
                 recursion failed to yield a closed form or a
                 closed-form approximation. We demonstrate the use of
                 genetic programming to rapidly obtain a high-accuracy
                 approximation with minimal assumptions about the
                 expected solution and without a need to specify
                 problem-specific parameterisations. We analyse both the
                 solution and the evolutionary process. This novel
                 application shows a promising way of using genetic
                 programming to solve recurrences in practical
  notes =        "GECCO-2004 A joint meeting of the thirteenth
                 international conference on genetic algorithms
                 (ICGA-2004) and the ninth annual genetic programming
                 conference (GP-2004)


Genetic Programming entries for Evan Kirshenbaum Henri J Suermondt