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Introducing Start Expression Genes to the Linkage Learning Genetic Algorithm

  • Ying-ping Chen
  • David E. Goldberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

This paper discusses the use of start expression genes and a modified exchange crossover operator in the linkage learning genetic algorithm (LLGA) that enables the genetic algorithm to learn the linkage of building blocks (BBs) through probabilistic expression (PE). The difficulty that the original LLGA encounters is shown with empirical results. Based on the observation, start expression genes and a modified exchange crossover operator are proposed to enhance the ability of the original LLGA to separate BBs and to improve LLGA’s performance on uniformly scaled problems. The effect of the modifications is also presented in the paper.

Keywords

Genetic Algorithm Genetic Programming Genetic Material Tight Linkage Probabilistic Expression 
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 2002

Authors and Affiliations

  • Ying-ping Chen
    • 1
  • David E. Goldberg
    • 2
  1. 1.Department of Computer Science and Department of General EngineeringUniversity of IllinoisUrbanaUSA
  2. 2.Department of General EngineeringUniversity of IllinoisUrbanaUSA

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