A C++ framework for geometric semantic genetic programming

Research output: Contribution to journalLetter

59 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)73-81
Number of pages9
JournalGenetic Programming And Evolvable Machines
Volume16
Issue number1
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • C++
  • Genetic programming
  • Geometric operators
  • Semantics

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