Learning Discretized Bayesian Networks with GOMEA
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70071-2_22
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2. Beuzen, T., Marshall, L., Splinter, K.D.: A comparison of methods for discretizing continuous variables in Bayesian networks. Environ. Model. Softw. 108, 61–66 (2018)
3. Bouter, A., Alderliesten, T., Witteveen, C., Bosman, P.A.N.: Exploiting linkage information in real-valued optimization with the real-valued gene-pool optimal mixing evolutionary algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017), pp. 705–712. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3071178.3071272
4. Bouter, A., Bosman, P.A.N.: A joint python/c++ library for efficient yet accessible black-box and gray-box optimization with gomea. In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO 2023), pp. 1864–1872. Association for Computing Machinery, New York (2023). https://doi.org/10.1145/3583133.3596361
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