Bloat Free Genetic Programming versus Classification Trees for Identification of Burned Areas in Satellite Imagery

  • Sara Silva
  • Maria J. Vasconcelos
  • Joana B. Melo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)


This paper compares Genetic Programming and Classification Trees on a problem of identification of burned areas in satellite imagery. Additionally, it studies how the most recently recognized bloat control technique, Operator Equalisation, affects the quality of the solutions provided by Genetic Programming. The merit of each approach is assessed not only by its classification accuracy, but also by the ability to predict the correctness of its own classifications, and the ability to provide solutions that are human readable and robust to data inaccuracies. The results reveal that both approaches achieve high accuracy with no overfitting, and that Genetic Programming can reveal some surprises and offer interesting advantages even on a simple problem so easily tackled by the popular Classification Trees. Operator Equalisation proved to be crucial.


Genetic Programming Satellite Imagery Symbolic Regression Program Length Standard Genetic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sara Silva
    • 1
    • 2
  • Maria J. Vasconcelos
    • 3
  • Joana B. Melo
    • 3
    • 4
  1. 1.INESC-ID LisboaPortugal
  2. 2.Center for Informatics and Systems of the University of CoimbraPortugal
  3. 3.Tropical Research InstituteLisbonPortugal
  4. 4.Instituto Superior de AgronomiaUTLPortugal

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