State-of-art of genetic programming applications in water-resources systems analysis
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State-of-art of genetic programming applications in water-resources systems analysis Sahar Mohammad-Azari & Omid Bozorg-Haddad & Hugo A. Loáiciga
Received: 23 August 2019 / Accepted: 16 December 2019 # Springer Nature Switzerland AG 2020
Abstract Evolutionary algorithms (EAs) have become competitive solvers of a wide variety of water-resources optimization problems. Genetic programming (GP) has become a leading EA since its inception in 1985. This paper reviews the state-ofthe-art of GP and its applications in water-resources systems analysis. A comprehensive knowledge about GP’s theory and modeling approach is essential for its successful application in water-resources systems analysis. This review presents variants of GP that have been proven useful in various applications to water resources problems. Several examples of applications of GP in water-resources systems analysis are herein presented. This review reveals GP’s capability and superiority compared to other conventional methods, which makes it suitable for solving a wide variety of water-related problems including rainfallrunoff modeling, streamflow sediment prediction, flood prediction and routing, evaporation and evapotranspiration forecasting, reservoir operation, groundwater modeling, water quality modeling, water demand forecasting, and water distribution systems.
Keywords Evolutionary programming . Geneexpression programming . Fixed-length gene genetic programming . Linear genetic programming
S. Mohammad-Azari Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran e-mail: [email protected]
Natural Resources, University of Tehran, Karaj, Tehran, Iran e-mail: [email protected]
O. Bozorg-Haddad (*) Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture &
Introduction The advent of evolutionary computation (EC) methods has revolutionized the field of water resources systems analysis and optimization. EC methods can tackle complex single-objective and multi-objective water resources systems problems that were previously intractable, as they may feature non-linear, discontinuous and non-differentiable, mixed-integer, and real variables of very large dimensionality (Koza 1994; Sreekanth and Datta 2010, 2011). EC methods refer to a class of computational methods inspired by natural processes of evolution. EC is applied in the form of evolutionary algorithms (EAs) such as the genetic algorithm (GA), genetic programming (GP), evolutionary programming (EP), evolution strategy, and differential evolution (ESDE)
H. A. Loáiciga Department of Geography, University of California, Santa Barbara, CA 93016-4060, USA e-mail: [email protected]
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(Babovic and Keijzer 2002). GP is a member of the EAs of relatively recent emergence. GP is applicable to a wide range of water-resources problems including rainfall-runoff prediction, evap
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