Logical genetic programming (LGP) application to water resources management

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@Article{ashofteh:2019:EMaA,

• author = "Parisa-Sadat Ashofteh and Omid Bozorg-Haddad and Hugo A. Loaiciga",
• title = "Logical genetic programming {(LGP)} application to water resources management",
• journal = "Environmental Monitoring and Assessment",
• year = "2019",
• volume = "192",
• number = "1",
• pages = "Article number: 34",
• keywords = "genetic algorithms, genetic programming, GP algorithm, LGP approach, Standard operating procedure (SOP) rule, Logical operators, Logical functions, Multi-conditional mathematical problem",
• URL = "http://link.springer.com/article/10.1007/s10661-019-8014-y",
• DOI = "doi:10.1007/s10661-019-8014-y",
• size = "11 pages",
• abstract = "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 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 superior performance in both examples, with LGP producing improvements of 74 and 42 percent in the objective functions of the mathematical and water resources examples, respectively, when compared with the GP 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.",

}

Genetic Programming entries for Parisa-Sadat Ashofteh Omid Bozorg Haddad Hugo A Loaiciga

Citations