Logical genetic programming (LGP) application to water resources management

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Genetic programming (GP) is a variant of evolutionary algorithms (EA). EAs are general-purpose search algorithms. Yet, GP does not solve multi-conditional problems satisfactorily. This study improves the GP’s predictive skill by development and integration of mathematical logical operators and functions to it. The proposed improvement is herein named logical genetic programming (LGP) whose performance is compared with that of GP using examples from the fields of mathematics and water resources. The results of the examples show the LGP’s superior performance in both examples, with LGP producing improvements of 74 and 42% in the objective functions of the mathematical and water resources examples, respectively, when compared with the GP’s results. The objective functions minimize the mean absolute error (MAE). The comparison of the LGP and GP results with alternative performance criteria demonstrate a better capability of the former algorithm in solving multi-conditional problems.

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  1. Ashofteh, P.-S., Bozorg-Haddad, O., & Mariño, M. A. (2013a). Climate change impact on reservoir performance indices in agricultural water supply. Journal of Irrigation and Drainage Engineering, 139(2), 85–97.

  2. 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.

  3. Ashofteh, P.-S., Bozorg-Haddad, O., Akbari-Alashti, H., and Mariño, M. A., (2015). “Determination of irrigation allocation policy under climate change by genetic programming”, Journal of Irrigation and Drainage Engineering (ASCE), 141(4), Doi:, 141 (4), 04014059.

  4. Babovic, V., & Keijzer, M. (2002). Rainfall runoff modeling based on genetic programming. Nordic Hydrology, 33(5), 331–346.

  5. Bozorg-Haddad, O., Afshar, A., & Mariño, M. A. (2006). Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resources Management, 20(5), 661–680.

  6. Bozorg-Haddad, O., Solgi, M., & Loáiciga, H. A. (2017). Meta-heuristic and evolutionary algorithms for engineering optimization. Hoboken: John Wiley & Sons.

  7. Cancelliere, A., Ancarani, A., & Rossi, G. (1998). Susceptibility of water supply reservoirs to drought conditions. Journal of Hydrologic Engineering, 3(2), 140–148.

  8. Danandeh Mehr, A., Nourani, V., Kahya, E., Hrnjica, B., Sattar, A. M. A., & Mundher Yaseen, Z. (2018). Genetic programming in water resources engineering: a state-of-the-art review. Journal of Hydrology, 566, 643–667.

  9. Fallah-Mehdipour, E., Bozorg-Haddad, O., & Marino, M. A. (2013). Prediction and simulation of monthly groundwater levels by genetic programming. Journal of Hydro-Environment Research, 7(4), 253–260.

  10. Fernandez, T., & Evett, M. (1998). Numeric mutation as an improvement to symbolic regression in genetic programming. Evolutionary Programming VII, Lecture Notes in Computer Science, Springer Verlag KG, 1447, 251–260.

  11. Giustolisi, O. (2004). Using genetic programming to determine Chèzy resistance coefficient in corrugated channels. Journal of Hydroinformatics, 6(3), 157–173.

  12. Golubski, W. (2002). New results on fuzzy regression by using genetic programming. Genetic Programming, Lecture Notes in Computer Science, Kinsale, Ireland, 2278, 308–315.

  13. Havlíček, V., Hanel, M., Máca, P., Kuráž, M., & Pech, P. (2013). Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting. Computing, 95(1), 363–380.

  14. Hu, T. S., Lam, K. C., & Ng, S. T. (2001). River flow time series prediction with a range dependent neural network. Hydrological Sciences Journal, 46(5), 729–745.

  15. Langdon, W. B. and Chen, T. (2018). “Genetic programming bibliography”, <>.

  16. Li, W. K., Wang, W. L., & Li, L. (2018). Optimization of water resources utilization by multi-objective moth-flame algorithm. Water Resources Management, 32(10), 3303–3316.

  17. Liong, S.-Y., Gautam, T. R., Khu, S. T., Babovic, V., Keijzer, M., & Muttil, N. (2007). Genetic programming: a new paradigm in rainfall runoff modeling. Journal of the American Water Resources Association, 38(3), 705–718.

  18. Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection (p. 819). Cambridge, Massachusets, London: MIT Press.

  19. Kramer, M. D. and Zhang, D. (2000). “GAPS: a genetic programming system”, The Twenty-Fourth Annual International Computer Software and Applications Conference, Taipei, 25-27 October, pp. 614-619.

  20. Loáiciga, H. A. (2002). Reservoir design and operation with variable lake hydrology. Journal of Water Resources Planning and Management, 128(6), 399–405.

  21. Loucks, D. P., Stedinger, J. R., & Haith, D. A. (1981). Water resources systems planning and analysis (p. 559). N. J., Prentice-Hall: Englewood Cliffs.

  22. Moghadam, S. H., Ashofteh, P.-S., & Loáiciga, H. A. (2019). Application of climate projections and Monte Carlo approach for the assessment of future river flow: case study of the Khorramabad River basin, Iran. Journal of Hydrologic Engineering, 24(7), 05019014.

  23. Morales, C. O. and Vázquez, K. R. (2004). “Symbolic regression problems by genetic programming with multi-branches”, advances in artificial intelligence, lecture notes in computer science, Springer-Verlag, Mexico City, Mexico, 26–30 April, 2972, 717–726.

  24. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900.

  25. Development Core Team, R. (2011). R: a language and environment for statistical computing. Vienna. R Foundation for Statistical Computing.

  26. Raman, H., & Chandramouli, V. (1996). Deriving a general operating policy for reservoirs using neural network. Journal of Water Resources Planning and Management, 122(5), 342–347.

  27. Savic, D. A., Walters, G. A., & Davidson, J. W. (1999). A genetic programming approach to rainfall-runoff modelling. Water Resources Management, 13(3), 219–231.

  28. Searson, D. P. Leahy, D. E., and 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.

  29. Sheng-Wu, X., & Wei-Wu, W. (2003). Point-tree structure genetic programming method for discontinuous function’s regression. Wuhan University Journal of Natural Sciences, 8(1), 323–326.

  30. Shokri A., Bozorg-Haddad O., Mariño M. A. (2014). “Multi-objective quantity–quality reservoir operation in sudden pollution”, Water Resources Management, 28(2):567–586, DOI:

  31. Silva, S. (2007). GPLAB: a genetic programming toolbox for Matlab, version 3 (pp. 13–15). ECOS-Evolutionary and Complex Systems Group: University of Coimbra, Portugal.

  32. Solgi, M., Bozorg-Haddad, O., & Loáiciga, H. A. (2017). The enhanced honey bee mating optimization algorithm for water resources optimization. Water Resources Management, 31(3).

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The authors thank Iran’s National Science Foundation (INSF) for its financial support on this research.

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Correspondence to Omid Bozorg-Haddad.

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Ashofteh, P., Bozorg-Haddad, O. & Loáiciga, H.A. Logical genetic programming (LGP) application to water resources management. Environ Monit Assess 192, 34 (2020) doi:10.1007/s10661-019-8014-y

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  • GP algorithm
  • LGP approach
  • Standard operating procedure (SOP) rule
  • Logical operators
  • Logical functions
  • Multi-conditional mathematical problem