Abstract
In current machine learning research, deep learning methodologies have become the prevalent approach across various domains, including decision-making processes. However, the interpretability of solutions generated by these algorithms remains a significant challenge, as these models do not inherently prioritize explainability. This lack of interpretability hampers the analysis of decision-making rationales. One potential remedy to this issue is the employment of Genetic Network Programming (GNP), a method within the evolutionary computing paradigm, known for its ability to generate more interpretable solutions. This study provides a concise overview of GNP, exploring its modifications and applications to demonstrate its utility in addressing the interpretability challenge in machine learning algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
Górriz, J.M., et al.: Computational approaches to explainable artificial intelligence: advances in theory, applications and trends. Inf. Fus. 100, 101945 (2023)
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)
Katagiri, H., Hirasawa, K., Hu, J., Murata, J.: Network structure oriented evolutionary model–genetic network programming–and its comparison with genetic programming. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, pp. 179–179 (2001)
Li, X., He, W., Hirasawa, K.: Adaptive genetic network programming. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1808–1815. IEEE (2014)
Li, X., Hirasawa, K.: A learning classifier system based on genetic network programming. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1323–1328. IEEE (2013)
Li, X., Li, B., Mabu, S., Hirasawa, K.: A continuous estimation of distribution algorithm by evolving graph structures using reinforcement learning. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)
Li, X., Mabu, S., Hirasawa, K.: An extended probabilistic model building genetic network programming using both of good and bad individuals. IEEJ Trans. Electr. Electron. Eng. 8(4), 339–347 (2013)
Li, X., Mabu, S., Hirasawa, K.: A novel graph-based estimation of the distribution algorithm and its extension using reinforcement learning. IEEE Trans. Evol. Comput. 18(1), 98–113 (2013)
Li, X., Mabu, S., Zhou, H., Shimada, K., Hirasawa, K.: Genetic network programming with estimation of distribution algorithms for class association rule mining in traffic prediction. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)
Li, X., Yang, M., Wu, S.: Niching genetic network programming with rule accumulation for decision making: an evolutionary rule-based approach. Expert Syst. Appl. 114, 374–387 (2018)
Mabu, S., Higuchi, T., Kuremoto, T.: Semisupervised learning for class association rule mining using genetic network programming. IEEJ Trans. Electr. Electron. Eng. 15(5), 733–740 (2020)
Mabu, S., Hirasawa, K., Hu, J.: Genetic network programming with reinforcement learning and its performance evaluation. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3103, pp. 710–711. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24855-2_81
Madokoro, H., Nix, S., Sato, K.: Automatic calibration of piezoelectric bed-leaving sensor signals using genetic network programming algorithms. Algorithms 14(4), 117 (2021)
Ramezanian, R., Peymanfar, A., Ebrahimi, S.B.: An integrated framework of genetic network programming and multi-layer perceptron neural network for prediction of daily stock return: an application in Tehran stock exchange market. Appl. Soft Comput. 82, 105551 (2019)
Xu, Y., Sun, Y., Ma, Z., Zhao, H., Wang, Y., Lu, N.: Attribute selection based genetic network programming for intrusion detection system. J. Adv. Comput. Intell. Intell. Inform. 26(5), 671–683 (2022)
Zhang, Y., Li, X., Yang, Y., Mabu, S., Jin, Y., Hirasawa, K.: Functionally distributed systems using parallel genetic network programming. In: Proceedings of SICE Annual Conference 2010, pp. 2626–2630. IEEE (2010)
Acknowledgments
This research is part of the PID2022-137451OB-I00 project funded by the MCIN/AEI/10.13039/501100011033 and by FSE+.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Roshanzamir, M., Alizadehsani, R., Moravvej, S.V., Joloudari, J.H., Alinejad-Rokny, H., Gorriz, J.M. (2024). Enhancing Interpretability in Machine Learning: A Focus on Genetic Network Programming, Its Variants, and Applications. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-61140-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-61139-1
Online ISBN: 978-3-031-61140-7
eBook Packages: Computer ScienceComputer Science (R0)