Genetic Programming and Evolvable Machines

, Volume 16, Issue 1, pp 73–81

A C++ framework for geometric semantic genetic programming

Letter

Abstract

Geometric semantic operators are new and promising genetic operators for genetic programming. They have the property of inducing a unimodal error surface for any supervised learning problem, i.e., any problem consisting in finding the match between a set of input data and known target values (like regression and classification). Thanks to an efficient implementation of these operators, it was possible to apply them to a set of real-life problems, obtaining very encouraging results. We have now made this implementation publicly available as open source software, and here we describe how to use it. We also reveal details of the implementation and perform an investigation of its efficiency in terms of running time and memory occupation, both theoretically and experimentally. The source code and documentation are available for download at http://gsgp.sourceforge.net.

Keywords

Genetic programming Semantics Geometric operators C++ 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Mauro Castelli
    • 1
    • 2
  • Sara Silva
    • 2
    • 3
    • 4
  • Leonardo Vanneschi
    • 1
    • 2
  1. 1.ISEGIUniversidade Nova de LisboaLisbonPortugal
  2. 2.INESC-ID, ISTUniversidade de LisboaLisbonPortugal
  3. 3.LabMAg, FCULUniversidade de LisboaLisbonPortugal
  4. 4.CISUCUniversidade de CoimbraCoimbraPortugal

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