The effect of spin-flip symmetry on the performance of the simple GA

  • Bart Naudts
  • Jan Naudts
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)


We use the one-dimensional nearest neighbor interaction functions (NNIs) to show how the presence of symmetry in a fitness function greatly influences the convergence behavior of the simple genetic algorithm (SGA). The effect of symmetry on the SGA supports the statement that it is not the amount of interaction present in a fitness function, measured e.g. by Davidor's epistasis variance and the experimental design techniques introduced by Reeves and Wright, which is important, but the kind of interaction. The NNI functions exhibit a minimal amount of second order interaction, are trivial to optimize deterministically and yet show a wide range of SGA behavior. They have been extensively studied in statistical physics; results from this field explain the negative effect of symmetry on the convergence behavior of the SGA. This note intends to introduce them to the GA-community.


Domain Wall Fitness Function Ising Model Convergence Behavior Order 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 1998

Authors and Affiliations

  • Bart Naudts
    • 1
  • Jan Naudts
    • 2
  1. 1.Department of Mathematics and Computer ScienceUniversity of Antwerp, RUCAAntwerpenBelgium
  2. 2.Department of PhysicsUniversity of Antwerp, UIAAntwerpenBelgium

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