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Pedro Larrañaga, Mikel Poza, Yosu Yurramendi, Roberto H. Murga, Cindy M.H. Kuijpers, "Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 912926, September, 1996.  
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@article{ 10.1109/34.537345, author = {Pedro Larrañaga and Mikel Poza and Yosu Yurramendi and Roberto H. Murga and Cindy M.H. Kuijpers}, title = {Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {18}, number = {9}, issn = {01628828}, year = {1996}, pages = {912926}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.537345}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters IS  9 SN  01628828 SP912 EP926 EPD  912926 A1  Pedro Larrañaga, A1  Mikel Poza, A1  Yosu Yurramendi, A1  Roberto H. Murga, A1  Cindy M.H. Kuijpers, PY  1996 KW  Bayesian network KW  genetic algorithm KW  structure learning KW  combinatorial optimization KW  performance analysis. VL  18 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—We present a new approach to structure learning in the field of Bayesian networks: We tackle the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures. We start by assuming an ordering between the nodes of the network structures. This assumption is necessary to guarantee that the networks that are created by the genetic algorithms are legal Bayesian network structures. Next, we release the ordering assumption by using a "repair operator" which converts illegal structures into legal ones. We present empirical results and analyze them statistically. The best results are obtained with an elitist genetic algorithm that contains a local optimizer.
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