Genetic programming in water resources engineering: A state-of-the-art review

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{DANANDEHMEHR:2018:JH,

• author = "Ali {Danandeh Mehr} and Vahid Nourani and Ercan Kahya and Bahrudin Hrnjica and Ahmed M. A. Sattar and Zaher Mundher Yaseen",
• title = "Genetic programming in water resources engineering: A state-of-the-art review",
• journal = "Journal of Hydrology",
• volume = "566",
• pages = "643--667",
• year = "2018",
• keywords = "genetic algorithms, genetic programming, Hydrology, Hydraulics, Hydroclimatology, Water resources engineering",
• ISSN = "0022-1694",
• DOI = "doi:10.1016/j.jhydrol.2018.09.043",
• URL = "http://www.sciencedirect.com/science/article/pii/S0022169418307376",
• abstract = "The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for automatic generation of computer programs. In recent decades, GP has been frequently applied on various kind of engineering problems and undergone speedy advancements. A number of studies have demonstrated the advantage of GP to solve many practical problems associated with water resources engineering (WRE). GP has a unique feature of introducing explicit models for nonlinear processes in the WRE, which can provide new insight into the understanding of the process. Considering continuous growth of GP and its importance to both water industry and academia, this paper presents a comprehensive review on the recent progress and applications of GP in the WRE fields. Our review commences with brief explanations on the fundamentals of classic GP and its advanced variants (including multigene GP, linear GP, gene expression programming, and grammar-based GP), which have been proven to be useful and frequently used in the WRE. The representative papers having wide range of applications are clustered in three domains of hydrological, hydraulic, and hydroclimatological studies, and outlined or discussed at each domain. Finally, this paper was concluded with discussions of the optimum selection of GP parameters and likely future research directions in the WRE are suggested",

}

Genetic Programming entries for Ali Danandeh Mehr Vahid Nourani Ercan Kahya Bahrudin Hrnjica Ahmed M Abdel Sattar Zaher Mundher Yaseen

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