Geometric Semantic Genetic Programming

  • Alberto Moraglio
  • Krzysztof Krawiec
  • Colin G. Johnson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7491)


Traditional Genetic Programming (GP) searches the space of functions/programs by using search operators that manipulate their syntactic representation, regardless of their actual semantics/behaviour. Recently, semantically aware search operators have been shown to outperform purely syntactic operators. In this work, using a formal geometric view on search operators and representations, we bring the semantic approach to its extreme consequences and introduce a novel form of GP – Geometric Semantic GP (GSGP) – that searches directly the space of the underlying semantics of the programs. This perspective provides new insights on the relation between program syntax and semantics, search operators and fitness landscape, and allows for principled formal design of semantic search operators for different classes of problems. We derive specific forms of GSGP for a number of classic GP domains and experimentally demonstrate their superiority to conventional operators.


Genetic Programming Boolean Function Output Vector Semantic Operator Search Operator 
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 2012

Authors and Affiliations

  • Alberto Moraglio
    • 1
  • Krzysztof Krawiec
    • 2
  • Colin G. Johnson
    • 3
  1. 1.School of Computer ScienceUniversity of BirminghamUK
  2. 2.Institute of Computing SciencePoznan University of TechnologyPoland
  3. 3.School of ComputingUniversity of KentUK

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