Evolutionary data-modelling of an innovative low reflective vertical quay
Created by W.Langdon from
gp-bibliography.bib Revision:1.8120
- @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