Model accuracy for hierarchical problems

Estimation of distribution algorithms, especially those using Bayesian network as their probabilistic model, have been able to solve many challenging optimization problems, including the class of hierarchical problems, competently. Since model-building constitute an important part of these algorithm...

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Bibliographic Details
Main Authors: Karshenas, H., Nikanjam, A., Helmi, B.H., Rahmani, A.T.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Bayesian methods
Computational complexity
Computational modeling
Couplings
EDAs
Electronic design automation and methodology
evaluation metries
Genetic algorithms
hierarchical problems
Laboratories
model accuracy
Sampling methods
Scalability
template matrix
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Summary:Estimation of distribution algorithms, especially those using Bayesian network as their probabilistic model, have been able to solve many challenging optimization problems, including the class of hierarchical problems, competently. Since model-building constitute an important part of these algorithms, finding ways to improve the quality of the models built during optimization is very beneficial. This in turn requires mechanisms to evaluate the quality of the models, as each problem has a large space of possible models. The efforts in this field are mainly concentrated on single-level problems, due to complex structure of hierarchical problems which makes them hard to treat. In order to extend model analysis to hierarchical problems, a model evaluation algorithm is proposed in this paper which can be applied to different problems. The results of applying the algorithm to two common hierarchical problems are also mentioned and described.
DOI:10.1109/ICICISYS.2009.5358041