Fluctuating Crosstalk as a Source of Deterministic Noise and Its Effects on GA Scalability

  • Kumara Sastry
  • Paul Winward
  • David E. Goldberg
  • Cláudio Lima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


This paper explores how fluctuating crosstalk in a deterministic fitness function introduces noise into genetic algorithms. We model fluctuating crosstalk or nonlinear interactions among building blocks via higher-order Walsh coefficients. The fluctuating crosstalk behaves like exogenous noise and can be handled by increasing the population size and run duration. This behavior holds until the strength of the crosstalk far exceeds the underlying fitness variance by a certain factor empirically observed. Our results also have implications for the relative performance of building-block-wise mutation over crossover.


Genetic Algorithm Convergence Time Crosstalk Signal Bayesian Optimization Algorithm Trap Function 
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

  • Kumara Sastry
    • 1
  • Paul Winward
    • 1
  • David E. Goldberg
    • 1
  • Cláudio Lima
    • 2
  1. 1.Illinois Genetic Algorithms Laboratory, Department of General EngineeringUniversity of Illinois at Urbana-Champaign 
  2. 2.DEEI-FCTUniversity of Algarve 

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