ESAGP – A Semantic GP Framework Based on Alignment in the Error Space

  • Stefano Ruberto
  • Leonardo Vanneschi
  • Mauro Castelli
  • Sara Silva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8599)


This paper introduces the concepts of error vector and error space, directly bound to semantics, one of the hottest topics in genetic programming. Based on these concepts, we introduce the notions of optimally aligned individuals and optimally coplanar individuals. We show that, given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. Thus, we introduce a genetic programming framework for symbolic regression called Error Space Alignment GP (ESAGP) and two of its instances: ESAGP-1, whose objective is to find optimally aligned individuals, and ESAGP-2, whose objective is to find optimally coplanar individuals. We also discuss how to generalize the approach to any number of dimensions. Using two complex real-life applications, we provide experimental evidence that ESAGP-2 outperforms ESAGP-1, which in turn outperforms both standard GP and geometric semantic GP. This suggests that “adding dimensions” is beneficial and encourages us to pursue the study in many different directions, that we summarize in the final part of the manuscript.


Root Mean Square Error Genetic Programming Error Vector Semantic Space Cartesian System 
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 2014

Authors and Affiliations

  • Stefano Ruberto
    • 1
    • 2
  • Leonardo Vanneschi
    • 3
  • Mauro Castelli
    • 3
  • Sara Silva
    • 2
    • 4
    • 5
  1. 1.GSSI, Gran Sasso Science Institute, INFNL’AquilaItaly
  2. 2.INESC-ID, ISTUniversity of LisbonLisbonPortugal
  3. 3.ISEGIUniversidade Nova de LisboaLisbonPortugal
  4. 4.LabMAg, FCULUniversity of LisbonLisbonPortugal
  5. 5.CISUCUniversidade de CoimbraCoimbraPortugal

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