Abstract
Genetic programming tends to optimize complicated structures producing human-competitive results; therefore, it is applied to a wide range of problems such as classification and regression. This work experimentally performs a comparative study of Genetic programming variants, namely gene expression, grammatical evolution, Cartesian, multi-expression programming, and stacked-based as general regression and classification solvers. The analyses will help to understand the strengths of each variant and identify the relative performance of variants that stand relative to each other for the given problem domains. To determine the performance difference between selected GP variants, hyper-parameter tuning was performed on each GP variant for each dataset to minimize the performance difference due to implementation. A total of 11 datasets were used in the experiments, seven from the regression benchmark suite, and four from the classification. The obtained results indicate that the choice of Genetic programming variant has an impact on the performance of regression and classification problems. Multi-expression programming exhibits outstanding performance as a regression and classification solver which scales graciously with problem size and complexity whereas other variants were problem-dependent. Future work could consider implementing a multi-expression paradigm with other Genetic programming variants such as grammatical evolution and gene expression programming.
This work is based on the research supported wholly/in part by the National Research Foundation of South Africa (Grant Numbers 138150). Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF.
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References
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Subprog. MIT Press, Cambridge (1994)
Oltean, M., Grosan, C.: A comparison of several linear genetic programming techniques. Complex Syst. 14(4), 285–314 (2003)
Sette, S., Boullart, L.: Genetic programming: principles and applications. Eng. Appl. Artif. Intell. 14(6), 727–736 (2001)
Ahvanooey, M.T., Li, Q., Wu, M., Wang, S.: A survey of genetic programming and its applications. KSII Trans. Internet Inf. Syst. (TIIS) 13(6), 1765–1794 (2019)
Aguilar-Ruiz, J.S., Riquelme, J.C., Toro, M.: Evolutionary learning of hierarchical decision rules. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 33(2), 324–331 (2003)
Langdon, W.B., Poli, R.: Fitness causes bloat. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds.) Soft Computing in Engineering Design and Manufacturing, pp. 13–22. Springer, London (1998). https://doi.org/10.1007/978-1-4471-0427-8_2
Sadrossadat, E., Basarir, H., Karrech, A., Durham, R., Fourie, A., Bin, H.: The optimization of cemented hydraulic backfill mixture design parameters for different strength conditions using artificial intelligence algorithms. In: Topal, E. (ed.) MPES 2019. SSGG, pp. 219–227. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33954-8_28
Gholampour, A., Gandomi, A.H., Ozbakkaloglu, T.: New formulations for mechanical properties of recycled aggregate concrete using gene expression programming. Constr. Build. Mater. 130, 122–145 (2017)
Yilmaz, S., Sen, S.: Early detection of botnet activities using grammatical evolution. In: Kaufmann, P., Castillo, P.A. (eds.) EvoApplications 2019. LNCS, vol. 11454, pp. 395–404. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16692-2_26
Contreras, I., Bertachi, A., Biagi, L., Vehí, J., Oviedo, S.: Using grammatical evolution to generate short-term blood glucose prediction models. In: KHD IJCAI, pp. 91–96 (2018)
Vasicek, Z.: Bridging the gap between evolvable hardware and industry using cartesian genetic programming. In: Stepney, S., Adamatzky, A. (eds.) Inspired by Nature. ECC, vol. 28, pp. 39–55. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67997-6_2
Elola, A., Del Ser, J., Bilbao, M.N., Perfecto, C., Alexandre, E., Salcedo-Sanz, S.: Hybridizing Cartesian genetic programming and harmony search for adaptive feature construction in supervised learning problems. Appl. Soft Comput. 52, 760–770 (2017)
Wang, H.L., Yin, Z.Y.: High performance prediction of soil compaction parameters using multi expression programming. Eng. Geol. 276 (2020)
Fallahpour, A., Wong, K.Y., Rajoo, S., Tian, G.: An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming. J. Clean. Prod. 283 (2021)
Helmuth, T., Spector, L.: General program synthesis benchmark suite. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1039–1046 (2015)
Chitty, D.M.: Faster GPU-based genetic programming using a two-dimensional stack. Soft. Comput. 21(14), 3859–3878 (2017)
UCI ML Repository dataset. http://archive.ics.uci.edu/ml/datasets. Accessed 25 Jan 2022
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
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Kuranga, C., Pillay, N. (2023). A Comparative Study of Genetic Programming Variants. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_32
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