Genetic Programming and Evolvable Machines

, Volume 9, Issue 1, pp 53–84

Sporadic model building for efficiency enhancement of the hierarchical BOA

  • Martin Pelikan
  • Kumara Sastry
  • David E. Goldberg
Original Paper

DOI: 10.1007/s10710-007-9052-8

Cite this article as:
Pelikan, M., Sastry, K. & Goldberg, D.E. Genet Program Evolvable Mach (2008) 9: 53. doi:10.1007/s10710-007-9052-8

Abstract

Efficiency enhancement techniques—such as parallelization and hybridization—are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once in every few iterations (generations), whereas in the remaining iterations, only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup, which decreases the asymptotic time complexity of model building in hBOA by a factor of \(\Uptheta(n^{0.26})\) to \(\Uptheta(n^{0.5}),\) where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, if model building is the bottleneck, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building. The paper also presents a dimensional model to provide a heuristic for scaling the structure-building period, which is the only parameter of the proposed sporadic model-building approach. The paper then tests the proposed method and the rule for setting the structure-building period on the problem of finding ground states of 2D and 3D Ising spin glasses.

Keywords

Bayesian optimization algorithm Hierarchical BOA Estimation of distribution algorithms Efficiency enhancement Sporadic model building 

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Martin Pelikan
    • 1
  • Kumara Sastry
    • 2
  • David E. Goldberg
    • 2
  1. 1.Missouri Estimation of Distribution Algorithms Laboratory, 321 CCB, Department of Mathematics and Computer ScienceUniversity of Missouri in St. LouisSt. LouisUSA
  2. 2.Illinois Genetic Algorithms Laboratory, 117 TB, Department of Industrial and Enterprise Systems EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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