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How Functional Dependency Adapts to Salience Hierarchy in the GAuGE System

  • Miguel Nicolau
  • Conor Ryan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2610)

Abstract

GAuGE is a position independent genetic algorithm that suffers from neither under nor over-specification, and uses a genotype to phenotype mapping process. By specifying both the position and the value of each gene, it has the potential to group important data together in the genotype string, to prevent it from being broken up and disrupted during the evolution process. To test this ability, GAuGE was applied to a set of problems with exponentially scaled salience. The results obtained demonstrate that GAuGE is indeed moving the more salient genes to the start of the genotype strings, creating robust individuals that are built in a progressive fashion from the left to the right side of the genotype.

Keywords

Genetic Algorithm Genetic Program Functional Dependency Grammatical Evolution Standard Genetic Algorithm 
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 2003

Authors and Affiliations

  • Miguel Nicolau
    • 1
  • Conor Ryan
    • 1
  1. 1.Department Of Computer Science And Information SystemsUniversity of LimerickIreland

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