Evolutionary data-modelling of an innovative low reflective vertical quay

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

@Article{Altomare:2013:JoH,

• author = "C. Altomare and X. Gironella and D. Laucelli",
• title = "Evolutionary data-modelling of an innovative low reflective vertical quay",
• journal = "Journal of Hydroinformatics",
• year = "2013",
• volume = "15",
• number = "3",
• pages = "763--779",
• month = "1 " # jul,
• keywords = "genetic algorithms, genetic programming, data-mining, evolutionary polynomial regression, low reflective vertical quay, wave reflection",
• URL = "https://iwaponline.com/jh/article-pdf/15/3/763/387059/763.pdf",
• DOI = "doi:10.2166/hydro.2012.219",
• size = "17 pages",
• abstract = "Vertical walls are commonly used as berthing structures. However, conventional vertical quays may have serious technical and environmental problems, as they reflect almost all the energy of the incident waves, thus affecting operational conditions and structural strength. These drawbacks can be overcome by the use of low reflective structures, but for some instances no theoretical equations exist to determine the relationship between the reflection coefficient and parameters that affect the structural response. Therefore, this study tries to fill this gap by examining the wave reflection of an absorbing gravity wall by means of evolutionary polynomial regression, a hybrid evolutionary modelling paradigm that combines the best features of conventional numerical regression and genetic programming. The method implements a multi-modelling approach in which a multi-objective genetic algorithm is used to get optimal models in terms of parsimony of mathematical expressions and fitting to data. A database of physical laboratory observations is used to predict the reflection as a function of a set of variables that characterize wave conditions and structure features. The proposed modelling paradigm proved to be a useful tool for data analysis and is able to find feasible explicit models featured by an appreciable generalization performance.",
• notes = "This content is only available as a PDF.",

}

Genetic Programming entries for Corrado Altomare Francesc Xavier Gironella i Cobos Daniele B Laucelli

Citations