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Sporadic model building for efficiency enhancement of hierarchical BOA (2007)
| Citations: | 17 - 9 self |
BibTeX
@MISC{Pelikan07sporadicmodel,
author = {Martin Pelikan and Kumara Sastry and David E. Goldberg},
title = {Sporadic model building for efficiency enhancement of hierarchical BOA},
year = {2007}
}
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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 Θ(n 0.26) to Θ(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 structurebuilding 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.
Keyphrases
sporadic model building model building hierarchical boa efficiency enhancement model parameter time complexity sporadic model-building approach ground state model structure efficiency enhancement technique structurebuilding period asymptotic complexity evaluation slowdown overall time complexity significant model-building speedup asymptotic time complexity evolutionary computation marginal probability hierarchical optimization problem sporadic model evolutionary algorithm ising spin glass structure-building period practical application important ingredient dimensional model probabilistic model hierarchical bayesian optimization algorithm important niche distribution algorithm problem size research area complex multivariate probabilistic model
