Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Subject: search.filters.subject.Genetic algorithm

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  • Book Part
    Citation - Scopus: 3
    Real-Time Flood Hydrograph Predictions Using Rating Curve and Soft Computing Methods (ga, Ann)
    (Elsevier, 2022) Tayfur, Gökmen; Tayfur, Gökmen; 03.03. Department of Civil Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    This chapter introduces hydraulic and hydrologic flood routing methods in natural channels. It details hydrological flood routing methods of the Rating Curve and Muskingum. Based on the rating curve method (RCM), it presents real-time flood hydrograph predictions using the genetic algorithm (GA-based RCM) model. In addition, it presents how to make real-time flood hydrograph predictions using the artificial neural network (ANN). The chapter briefly introduces the basics of GA and details how to calibrate and validate the GA-based RCM model using measured real-time flood hydrographs. Similarly, after giving the basics of ANN, it shows how to train and test the ANN model using measured hydrographs. Real hydrograph simulations by the RCM, GA-based RCM, and ANN are presented, and merits of each model are discussed. © 2023 Elsevier Inc. All rights reserved.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Trait-based heterogeneous populations plus (TbHP+) genetic algorithm
    (Elsevier Ltd., 2009) Tayfur, Gökmen; Sevil, Hakkı Erhan; Özdemir, Serhan; Özdemir, Serhan; Tayfur, Gökmen; 03.03. Department of Civil Engineering; 03.10. Department of Mechanical Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    This study developed a variant of genetic algorithm (GA) model called the trait-based heterogeneous populations plus (TbHP+). The developed TbHP+ model employs a memory concept in the form of immunity and instinct to provide the populations with a more efficient guidance. Also, it has an ability to vary the number of individuals during the search process, thus allowing an automatic determination of the size of the population based on the individual qualities such as character fitness and credit for immunity. The algorithm was tested against the classical GA model in convergence and minimum error performance. For this purpose, 5 different mathematical functions from the literature were employed. The selected functions have different topological characteristics, ranging from simple convex curves with 2 variables to complex trigonometric ones having several hilly shapes with more than 2 variables. The developed model and the classical GA model were applied to finding the global minima of the functions. The comparison of the results revealed that the developed TbHP+ model outperformed the classical GA in faster convergence and minimum errors, which may be explained by the adaptive nature of the new paradigm.