Abstract
Artificial hydrocarbon networks (AHN) is a supervised learning model that is loosely inspired on the interactions of molecules in organic compounds. This method is able to model data in a hierarchical and robust way. However, the original training algorithm is very time-consuming. Recently, novel training algorithms have been applied, including evolutionary learning. Particularly, this training algorithm employed particle swarm optimization (PSO), as part of the procedure. In this paper, we present a benchmark of other meta-heuristic optimization algorithms implemented on the training method for AHN. In this study, PSO, harmony search algorithm, cuckoo search, grey wolf optimization and whale optimization algorithm, were tested. The experimental results were done using public data sets on regression and binary classification problems. From the results, we concluded that the best algorithm was cuckoo search optimization for regression problems, while there is no evidence that one of the algorithms performed better for binary classification problems.
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References
Abdullah, S., Burke, E.K., McCollum, B.: A hybrid evolutionary approach to the university course timetabling problem. In: 2007 IEEE Congress on Evolutionary Computation. pp. 1764–1768. IEEE (2007)
Asuncion, A., Newman, D.: Uci machine learning repository (2007)
Bengio, Y.: Gradient-based optimization of hyperparameters. Neural Comput. 12(8), 1889–1900 (2000)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)
Glover, F.W., Kochenberger, G.A.: Handbook of metaheuristics. Springer Science & Business Media, Berlin (2006)
Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Trans. Evol. Comput. 11(1), 56–76 (2007)
Harizan, S., Kuila, P.: Evolutionary algorithms for coverage and connectivity problems in wireless sensor networks: a study. In: Das, S.K., Samanta, S., Dey, N., Kumar, R. (eds.) Design Frameworks for Wireless Networks. LNNS, vol. 82, pp. 257–280. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9574-1_11
Karimi, F., Lotfi, S., Izadkhah, H.: Multiplex community detection in complex networks using an evolutionary approach. Expert Syst. Appl. 146, 113184 (2020)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks. vol. 4, pp. 1942–1948. IEEE (1995)
Kuila, P., Gupta, S.K., Jana, P.K.: A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol. Comput. 12, 48–56 (2013)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Software 95, 51–67 (2016). https://doi.org/10.1016/j.advengsoft.2016.01.008
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Software 69, 46–61 (2014). https://doi.org/10.1016/j.advengsoft.2013.12.007
Ponce, H., de Campos Souza, P.V., Guimarães, A.J., González-Mora, G.: Stochastic parallel extreme artificial hydrocarbon networks: an implementation for fast and robust supervised machine learning in high-dimensional data. Eng. Appl. Artif. Intell. 89, 103427 (2020)
Ponce, H., González-Mora, G., Morales-Olvera, E., Souza, P.: Development of fast and reliable nature-inspired computing for supervised learning in high-dimensional data. In: Rout, M., Rout, J.K., Das, H. (eds.) Nature Inspired Computing for Data Science. SCI, vol. 871, pp. 109–138. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33820-6_5
Ponce, H., Ponce, P.: Artificial organic networks. In: Electronics, Robotics and Automotive Mechanics Conference (CERMA). pp. 29–34. IEEE (2011)
Ponce, H., Ponce, P.: Artificial hydrocarbon networks: a new algorithm bio-inspired on organic chemistry. Int. J. Artif. Intell. Comput. Res. 4(1), 39–51 (2012)
Ponce-Espinosa, H., Ponce-Cruz, P., Molina, A.: Artificial organic networks. Artificial Organic Networks. SCI, vol. 521, pp. 53–72. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02472-1_3
Rodriguez, J.D., Perez, A., Lozano, J.A.: Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 569–575 (2009)
Sinha, A., Malo, P., Deb, K.: A review on bilevel optimization: from classical to evolutionary approaches and applications. IEEE Trans. Evol. Comput. 22(2), 276–295 (2017)
Sun, J., Garibaldi, J.M., Hodgman, C.: Parameter estimation using metaheuristics in systems biology: a comprehensive review. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(1), 185–202 (2011)
Yang, X.S., Deb, S.: Cuckoo search via lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). pp. 210–214. IEEE (2009)
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Ponce, H., Souza, P. (2020). A Comparative Analysis of Evolutionary Learning in Artificial Hydrocarbon Networks. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Soft Computing. MICAI 2020. Lecture Notes in Computer Science(), vol 12468. Springer, Cham. https://doi.org/10.1007/978-3-030-60884-2_17
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