Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Castelli:2013:ESA,

• author = "Mauro Castelli and Leonardo Vanneschi and Sara Silva",
• title = "Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators",
• journal = "Expert Systems with Applications",
• volume = "40",
• number = "17",
• pages = "6856--6862",
• year = "2013",
• ISSN = "0957-4174",
• DOI = "doi:10.1016/j.eswa.2013.06.037",
• URL = "http://www.sciencedirect.com/science/article/pii/S0957417413004326",
• keywords = "genetic algorithms, genetic programming, High performance concrete, Strength prediction, Artificial intelligence, Geometric operators, Semantics, Weka, Linear regression, Square Regression, Isotonic Regression, Radial Basis Function Network, RBF, SVM, ANN",
• abstract = "Concrete is a composite construction material made primarily with aggregate, cement, and water. In addition to the basic ingredients used in conventional concrete, high-performance concrete incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, high-performance concrete is a highly complex material and modelling its behaviour represents a difficult task. In this paper, we propose an intelligent system based on Genetic Programming for the prediction of high-performance concrete strength. The system we propose is called Geometric Semantic Genetic Programming, and it is based on recently defined geometric semantic genetic operators for Genetic Programming. Experimental results show the suitability of the proposed system for the prediction of concrete strength. In particular, the new method provides significantly better results than the ones produced by standard Genetic Programming and other machine learning methods, both on training and on out-of-sample data.",
• notes = "page6861 'better (than) other well-known machine learning techniques'",

}

Genetic Programming entries for Mauro Castelli Leonardo Vanneschi Sara Silva

Citations