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Examining Mutation Landscapes in Grammar Based Genetic Programming

  • Eoin Murphy
  • Michael O’Neill
  • Anthony Brabazon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)

Abstract

Representation is a very important component of any evolutionary algorithm. Changing the representation can cause an algorithm to perform very differently. Such a change can have an effect that is difficult to understand. This paper examines what happens to the grammatical evolution algorithm when replacing the commonly used context-free grammar representation with a tree-adjunct grammar representation. We model the landscapes produced when using integer flip mutation with both representations and compare these landscapes using visualisation methods little used in the field of genetic programming.

Keywords

Genetic Programming Landscape Model Derivation Tree Symbolic Regression Grammatical Evolution 
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 2011

Authors and Affiliations

  • Eoin Murphy
    • 1
  • Michael O’Neill
    • 1
  • Anthony Brabazon
    • 1
  1. 1.Natural Computing Research and Applications GroupUniveristy College DublinIreland

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