Application of Artificial Neural Network and Genetic Programming in Civil Engineering

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

@InCollection{Samui:2016:CEECMTA.44,

• author = "Pijush Samui and Dhruvan Choubisa and Akash Sharda",
• title = "Application of Artificial Neural Network and Genetic Programming in Civil Engineering",
• booktitle = "Civil and Environmental Engineering: Concepts, Methodologies, Tools, and Applications",
• publisher = "IGI Global",
• year = "2016",
• chapter = "44",
• pages = "1022--1036",
• month = jan,
• keywords = "genetic algorithms, genetic programming, ANN, ANFIS",
• ISBN = "1-4666-9619-2",
• URL = "https://www.igi-global.com/chapter/application-of-artificial-neural-network-and-genetic-programming-in-civil-engineering/144536",
• DOI = "doi:10.4018/978-1-4666-9619-8.ch044",
• abstract = "This chapter examines the capability of Genetic Programming (GP) and different Artificial Neural Network (ANN) (Backpropagation [BP] and Generalised Regression Neural Network [GRNN]) models for prediction of air entrainment rate (QA) of triangular sharp-crested weir. The basic principal of GP has been taken from the concept of Genetic Algorithm (GA). Discharge (Q), drop height (h), and angle in triangular sharp-crested weir (theta) are considered as inputs of BP, GRNN, and GP. Coefficient of Correlation (R) has been used to assess the performance of developed GP, BP, and GRNN models. For a perfect model, the value of R should be close to one. A sensitivity analysis has been carried out to determine the effect of each input parameter. This chapter presents a comparative study between the developed BP, GRNN, and GP models",
• notes = "VIT University, India",

}

Genetic Programming entries for Pijush Samui Dhruvan Choubisa Akash Sharda

Citations