Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Sporadic model building for efficiency enhancement of the hierarchical BOA
Genetic Programming and Evolvable Machines
From mating pool distributions to model overfitting
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Model complexity vs. performance in the bayesian optimization algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
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Bayesian Optimization Algorithm (BOA) has been used with different local structures to represent more complex models and a variety of scoring metrics to evaluate Bayesian network. But the combinatorial effects of these elements on the performance of BOA have not been investigated yet. In this paper the performance of BOA is studied using two criteria: Number of fitness evaluations and structural accuracy of the model. It is shown that simple exact local structures like CPT in conjunction with complexity penalizing BIC metric outperforms others in terms of model accuracy. But considering number of fitness evaluations (efficiency) of the algorithm, CPT with other complexity penalizing metric K2P performs better.