Linkage Learning Using Graphical Markov Model Structure: An Experimental Study

  • Eunice Esther Ponce-de-Leon-Senti
  • Elva Diaz-Diaz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)

Abstract

Linkage learning to identify the interaction structure of an optimization problem helps the evolutionary algorithms to search the optimal solution. Learning the structure of a distribution representing the interactions of the optimization problem is equivalent to learning the variables of the problem linkage. The objective of this paper is to test the efficiency of an EDA that use Boltzmann selection and a cliqued Gibbs sampler (named Adaptive Extended Tree Cliqued - EDA (AETCEDA)) to learn the linkage of the problem and generate samples. Some optimization problems difficult for the Genetic Algorithms are used to test the proposed algorithm. As results of the experiment is to emphasize that the difficulty of the optimization, as assessed by the number of evaluations, is proportional to the sizes of the cliques of the learned models, that in time, is proportional to the structure of the test problem.

Keywords

Test Problem Graphical Model Bayesian Optimization Learn Index Bayesian Optimization Algorithm 
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 2013

Authors and Affiliations

  • Eunice Esther Ponce-de-Leon-Senti
    • 1
  • Elva Diaz-Diaz
    • 1
  1. 1.Autonomous University of AguascalientesAguascalientesMexico

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