Enhancing the Performance of Maximum–Likelihood Gaussian EDAs Using Anticipated Mean Shift

  • Peter A. N. Bosman
  • Jörn Grahl
  • Dirk Thierens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


Many Estimation–of–Distribution Algorithms use maximum-likelihood (ML) estimates. For discrete variables this has met with great success. For continuous variables the use of ML estimates for the normal distribution does not directly lead to successful optimization in most landscapes. It was previously found that an important reason for this is the premature shrinking of the variance at an exponential rate. Remedies were subsequently successfully formulated (i.e. Adaptive Variance Scaling (AVS) and Standard–Deviation Ratio triggering (SDR)). Here we focus on a second source of inefficiency that is not removed by existing remedies. We then provide a simple, but effective technique called Anticipated Mean Shift (AMS) that removes this inefficiency.


Premature Convergence Density Contour Distribution Algorithm Iterate Density Ridge 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 2008

Authors and Affiliations

  • Peter A. N. Bosman
    • 1
  • Jörn Grahl
    • 2
  • Dirk Thierens
    • 3
  1. 1.Centre for Mathematics and Computer ScienceAmsterdamThe Netherlands
  2. 2.University of MannheimMannheimGermany
  3. 3.Department of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands

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