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Doing Genetic Algorithms the Genetic Programming Way

  • Conor Ryan
  • Miguel Nicolau
Part of the Genetic Programming Series book series (GPEM, volume 6)

Abstract

This paper describes the GAuGE system, Genetic Algorithms using Grammatical Evolution, which uses Grammatical Evolution to perform as a position independent Genetic Algorithm. Gauge has already been successfully applied to domains such as bit level, sorting and regression problems, and our experience suggests that it evolves individuals with a similar dynamic to Genetic Programming. That is, there is a hierarchy of dependency within the individual, and, as evolution progresses, those parts at the top of the hierarchy become fixed across a population. We look at the manner in which the population evolves the representation at the same time as optimising the problem, and demonstrate there is a definite emergence of representation.

Keywords

Genetic Algorithm Genetic Program Production Rule Binary String Ripple Effect 
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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References

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Conor Ryan
    • 1
  • Miguel Nicolau
    • 1
  1. 1.CSIS DepartmentUniversity of LimerickIreland

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