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The Effect of Building Block Construction on the Behavior of the GA in Dynamic Environments: A Case Study Using the Shaky Ladder Hyperplane-Defined Functions

  • William Rand
  • Rick Riolo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

The shaky ladder hyperplane-defined functions (sl-hdf’s) are a test suite utilized for exploring the behavior of the genetic algorithm (GA) in dynamic environments. We present three ways of constructing the sl-hdf’s by manipulating the way building blocks are constructed, combined, and changed. We examine the effect of the length of elementary building blocks used to create higher building blocks, and the way in which those building blocks are combined. We show that the effects of building block construction on the behavior of the GA are complex. Our results suggest that construction routines which increase the roughness of the changes in the environment allow the GA to perform better by preventing premature convergence. Moreover, short length elementary building blocks permit early rapid progress.

Keywords

Genetic Algorithm Dynamic Environment Test Suite Construction Method Premature Convergence 
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 2006

Authors and Affiliations

  • William Rand
    • 1
  • Rick Riolo
    • 2
  1. 1.Northwestern Institute on Complex SystemsNorthwestern UniversityEvanstonUSA
  2. 2.Center for the Study of Complex SystemsUniversity of MichiganAnn ArborUSA

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