Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms

Pedro Larrañaga, Roberto H. Murga, Mikel Poza, Cindy M. H. Kuijpers
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:310-316, 1995.

Abstract

This paper demonstrates how Genetic Algorithms can be used to discover the structure of a Bayesian Network from a given database with cases. The results presented, were obtained by applying four different types of Genetic Algorithms - SSGA (Steady State Genetic Algorithm), GAe λ (Genetic Algorithm elistist of degree λ ), hSSGA (hybrid Steady State Genetic Algorithm) and the hGAe λ (hybrid Genetic Algorithm elitist of degree λ ) - to simulations of the ALARM Network. The behaviour of the mentioned algorithms is studied with respect to their parameters.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-larranaga95a, title = {Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms}, author = {Larra{\~{n}}aga, Pedro and Murga, Roberto H. and Poza, Mikel and Kuijpers, Cindy M. H.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {310--316}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/larranaga95a/larranaga95a.pdf}, url = {https://proceedings.mlr.press/r0/larranaga95a.html}, abstract = {This paper demonstrates how Genetic Algorithms can be used to discover the structure of a Bayesian Network from a given database with cases. The results presented, were obtained by applying four different types of Genetic Algorithms - SSGA (Steady State Genetic Algorithm), GAe $\lambda$ (Genetic Algorithm elistist of degree $\lambda$ ), hSSGA (hybrid Steady State Genetic Algorithm) and the hGAe $\lambda$ (hybrid Genetic Algorithm elitist of degree $\lambda$ ) - to simulations of the ALARM Network. The behaviour of the mentioned algorithms is studied with respect to their parameters.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms %A Pedro Larrañaga %A Roberto H. Murga %A Mikel Poza %A Cindy M. H. Kuijpers %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-larranaga95a %I PMLR %P 310--316 %U https://proceedings.mlr.press/r0/larranaga95a.html %V R0 %X This paper demonstrates how Genetic Algorithms can be used to discover the structure of a Bayesian Network from a given database with cases. The results presented, were obtained by applying four different types of Genetic Algorithms - SSGA (Steady State Genetic Algorithm), GAe $\lambda$ (Genetic Algorithm elistist of degree $\lambda$ ), hSSGA (hybrid Steady State Genetic Algorithm) and the hGAe $\lambda$ (hybrid Genetic Algorithm elitist of degree $\lambda$ ) - to simulations of the ALARM Network. The behaviour of the mentioned algorithms is studied with respect to their parameters. %Z Reissued by PMLR on 01 May 2022.
APA
Larrañaga, P., Murga, R.H., Poza, M. & Kuijpers, C.M.H.. (1995). Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:310-316 Available from https://proceedings.mlr.press/r0/larranaga95a.html. Reissued by PMLR on 01 May 2022.

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