Limiting the Number of Fitness Cases in Genetic Programming Using Statistics

  • Mario Giacobini
  • Marco Tomassini
  • Leonardo Vanneschi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)


Fitness evaluation is often a time consuming activity in genetic programming applications and it is thus of interest to find criteria that can help in reducing the time without compromising the quality of the results. We use well-known results in statistics and information theory to limit the number of fitness cases that are needed for reliable function reconstruction in genetic programming. By using two numerical examples, we show that the results agree with our theoretical predictions. Since our approach is problem-independent, it can be used together with techniques for choosing an efficient set of fitness cases.


Genetic Programming Boolean Function Target Function Function Entropy Discrete Random Variable 
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 2002

Authors and Affiliations

  • Mario Giacobini
    • 1
  • Marco Tomassini
    • 1
  • Leonardo Vanneschi
    • 1
  1. 1.Computer Science InstituteUniversity of LausanneLausanneSwitzerland

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