Enhancing Interpretability in Machine Learning: A Focus on Genetic Network Programming, Its Variants, and Applications

Author:

Roshanzamir Mohamad,Alizadehsani Roohallah,Moravvej Seyed Vahid,Joloudari Javad Hassannataj,Alinejad-Rokny Hamid,Gorriz Juan M.

Publisher

Springer Nature Switzerland

Reference19 articles.

1. Chen, Y., Mabu, S., Hirasawa, K.: Genetic network programming with reinforcement learning and its application to creating stock trading rules. In: Machine Learning. IntechOpen (2009)

2. Foss, F., Stenrud, T., Haddow, P.C.: Investigating genetic network programming for multiple nest foraging. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. IEEE (2021)

3. Gonzales, E., Shimada, K., Mabu, S., Hirasawa, K., Hu, J.: Genetic network programming with parallel processing for association rule mining in large and dense databases. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1512–1512 (2007)

4. Górriz, J.M., et al.: Computational approaches to explainable artificial intelligence: advances in theory, applications and trends. Inf. Fus. 100, 101945 (2023)

5. Hirasawa, K., Okubo, M., Katagiri, H., Hu, J., Murata, J.: Comparison between genetic network programming (GNP) and genetic programming (GP). In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), vol. 2, pp. 1276–1282. IEEE (2001)

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