Development of logical operator’s in common genetic programming (CGP) and its calibration in SOP rule

Document Type : Research Paper


1 Assistant Professor, Department of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran

2 Professor, Department of Irrigation & Reclamation, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran


Common genetic programming (CGP) with considering empirical data (observed) improves and evolves estimated data (calculated). However, CGP is not able to solve multi-conditional problems with satisfactory performance. In this study, capability of the CGP is improved through development and integration of mathematical functions and logical operators on it. Proposed algorithm is called logic genetic programming (LGP) that its performance improvement is investigated in comparison with CGP in field of water resources. Results show that LGP capability, is more effective and more efficient than CGP, so that LGP improves objective function by 39 percent compared to CGP, in extraction of standard operating policy (SOP) [with minimization of mean absolute error (MAE)]. Comparison of algorithms results using the evaluation criteria indicate that LGP algorithm in SOP reconstruction resulted in a 22% decrease in RMSE and a 1% increase in NSE compared to CGP.


1. قادری، ک.، زلقی، آ.، و بختیاری، ب. (1393). بهینه‌سازی بهره‌برداری از سیستم چند مخزنی با استفاده از الگوریتم تکامل رقابتی جوامع (SCE) (مطالعه موردی: حوضه کرخه). مدیریت آب و آبیاری، 4 (2): 228-215.
2. کمالی، پ.، ابراهیمیان، ح.، و وردی‌نژاد، و.ر. (1394). ارزیابی و مقایسه روش بهینه‌سازی چندسطحی و مدل IPARM در تخمین پارامترهای نفوذ در آبیاری جویچه‌ای. مدیریت آب و آبیاری. 5 (1): 54-43.
3. مولوی، ح.، لیاقت، ع.، و نظری، ب. (1395). ارزیابی سیاست‌های اصلاح الگوی کشت و مدیریت کم آبیاری با استفاده از مدل‌سازی پویایی سیستم (مطالعه موردی: حوضه آبریز ارس). مدیریت آب و آبیاری، 6 (2): 236-217.
4. Aryafar, A., Khosravi, V., Zarepourfard, H. & Rooki, R. (2019). Evolving genetic programming and other AI-based models for estimating groundwater quality parameters of the Khezri plain, Eastern Iran. Environmental Earth Science, 78 (3), 1-13.
5. Ashofteh, P.-S., Bozorg-Haddad, O. & Mariño, M. A. (2013a). Climate change impact on reservoir performance indices in agricultural water supply. Irrigation and Drainage Engineering, 139 (2), 85-97.
6. Ashofteh, P.-S., Bozorg-Haddad, O. & Mariño, M. A. (2013b). Scenario assessment of streamflow simulation and its transition probability in future periods under climate change. Water Resources Management, 27 (1), 255-274.
7. Cancelliere, A., Ancarani, A. & Rossi, G. (1998). Susceptibility of water supply reservoirs to drought conditions. Hydrologic Engineering, 3(2), 140-148.
8. Chadalawada, J., Havlicek, V. & Babovic, V. (2017). A genetic programming approach to system identification of rainfall-runoff models. Water Resources Management, 31(12), 3975-3992.
9. Golubski, W. (2002). New results on fuzzy regression by using genetic programming. Genetic Programming, Lecture Notes in Computer Science. Kinsale. Ireland. 2278, 308-315.
10. Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. MIT Press. Cambridge. Massachusets. London. England. 1-819.
11. Kramer, M. D. & Zhang, D. (2000). GAPS: A genetic programming system. The Twenty-Fourth Annual International Computer Software and Applications Conference. Taipei. 25-27 October. 614-619.
12. Loucks, D. P., Stedinger, J. R. & Haith, D. A. (1981). Water resources systems planning and analysis. Englewood Cliffs. N. J. Prentice-Hall. 1-559.
13. Morales, C. O. & Vázquez, K. R. (2004). Symbolic regression problems by genetic programming with multi-branches. Adv. in Art. Int. Lec. Not. in Com. Sci. Springer-Verlag. Mexico City. Mexico. 26-30 April. 2972, 717-726.
14. Raman, H. & Chandramouli, V. (1996). Deriving a general operating policy for reservoirs using neural network. Water Resources Planning and Management, 122(5), 342-347.
15. Searson, D. P., Leahy, D. E. & Willis, M. J. (2011). Predicting the toxicity of chemical compounds using GPTIPS: A free genetic programming toolbox for MATLAB, Intelligent Control and Computer Engineering. Lecture Notes in Electrical Engineering. Springer. 70: 83-93.
16. Sepahvand, R., Safavi, H. R. & Rezaei, F. (2019). Multi-objective planning for conjunctive use of surface and ground water resources using programming. Water Resources Management, 33(6), 2123-2137.
17. Silva, S. (2007). GPLAB: A genetic programming toolbox for Matlab, Version 3. ECOS-Evo. and Com. Sys. Gro. University of Coimbra. Portugal. 13-15.
18. Sheng-Wu, X. & Wei-Wu, W. (2003). Point-tree structure genetic programming method for discontinuous function’s regression. Wuhan University Natural Sciences, 8, 323-326.
19. Tayfur, G. (2017). Modern optimization methods in water resources planning, engineering and management. Water Resources Management, 31(10), 3205-3233.