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Exploring Grammatical Modification with Modules in Grammatical Evolution

  • John Mark Swafford
  • Michael O’Neill
  • Miguel Nicolau
  • Anthony Brabazon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)

Abstract

There have been many approaches to modularity in the field of evolutionary computation, each tailored to function with a particular representation. This research examines one approach to modularity and grammar modification with a grammar-based approach to genetic programming, grammatical evolution (GE). Here, GE’s grammar was modified over the course of an evolutionary run with modules in order to facilitate their appearance in the population. This is the first step in what will be a series of analysis on methods of modifying GE’s grammar to enhance evolutionary performance. The results show that identifying modules and using them to modify GE’s grammar can have a negative effect on search performance when done improperly. But, if undertaken thoughtfully, there are possible benefits to dynamically enhancing the grammar with modules identified during evolution.

Keywords

Genetic Programming Average Fitness Parse Tree Parent Individual Generation Generation 
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

  • John Mark Swafford
    • 1
  • Michael O’Neill
    • 1
  • Miguel Nicolau
    • 1
  • Anthony Brabazon
    • 2
  1. 1.Natural Computing Research & Applications Group Complex and Adaptive Systems Laboratory School of Computer Science & InformaticsUniversity College DublinIreland
  2. 2.Natural Computing Research & Applications Group Complex and Adaptive Systems Laboratory School of BusinessUniversity College DublinIreland

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