Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support
Data Mining and Knowledge Discovery
Toward More Powerful Recombinations
Proceedings of the 6th International Conference on Genetic Algorithms
A bibliography of temporal, spatial and spatio-temporal data mining research
ACM SIGKDD Explorations Newsletter
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
Artificial Intelligence in Medicine
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
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Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of these search methods to temporal Bayesian networks [Tucker01]. A characteristic of many datasets from medical to geographical data is the spatial arrangement of variables. In this paper we investigate a set of operators that have been designed to exploit the spatial nature of such data in order to learn dynamic Bayesian networks more efficiently. We test these operators on synthetic data generated from a Gaussian network where the architecture is based upon a Cartesian coordinate system, and real-world medical data taken from visual field tests of patients suffering from ocular hypertension.