Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning Bayesian networks with local structure
Learning in graphical models
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Sporadic model building for efficiency enhancement of hierarchical BOA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Parallel EDAs to create multivariate calibration models for quantitative chemical applications
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Design of multithreaded estimation of distribution algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Hierarchical BOA solves ising spin glasses and MAXSAT
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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It has been shown that model building in the hierarchical Bayesian optimization algorithm (hBOA) can be efficiently parallelized by randomly generating an ancestral ordering of the nodes of the network prior to learning the network structure and allowing only dependencies consistent with the generated ordering. However, it has not been thoroughly shown that this approach to restricting probabilistic models does not affect scalability of hBOA on important classes of problems. This paper demonstrates that although the use of a random ancestral ordering restricts the structure of considered models to allow efficient parallelization of model building, its effects on hBOA performance and scalability are negligible.