A New Evolutionary Approach to Geotechnical and Geo-Environmental Modelling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InCollection{Hussain:2015:hbgpa,
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author = "Mohammed S. Hussain and Alireza Ahangar-asr and
Youliang Chen and Akbar A. Javadi",
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title = "A New Evolutionary Approach to Geotechnical and
Geo-Environmental Modelling",
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booktitle = "Handbook of Genetic Programming Applications",
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publisher = "Springer",
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year = "2015",
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editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
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chapter = "19",
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pages = "483--499",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-20882-4",
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DOI = "doi:10.1007/978-3-319-20883-1_19",
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abstract = "In many cases, models based on certain laws of physics
can be developed to describe the behaviour of physical
systems. However, in case of more complex phenomena
with less known or understood contributing parameters
or variables the physics-based modelling techniques may
not be applicable. Evolutionary Polynomial Regression
(EPR) offers a new way of rendering models, in the form
of easily interpretable polynomial equations,
explicitly expressing the relationship between
contributing parameters of a system of complex nature,
and the behaviour of the system. EPR is a recently
developed hybrid regression method that provides
symbolic expressions for models and works with formulae
based on pseudo-polynomial expressions. In this chapter
the application of EPR to two important geotechnical
and geo-environmental engineering systems is presented.
These systems include thermo-mechanical behaviour of
unsaturated soils and optimisation of performance of an
aquifer system subjected to seawater intrusion.
Comparisons are made between the EPR model predictions
and the actual measured or synthetic data. The results
show that the proposed methodology is able to develop
highly accurate models with excellent capability of
reflecting the real and expected physical effects of
the contributing parameters on the performance of the
systems. Merits and advantages of the suggested
methodology are highlighted.",
- }
Genetic Programming entries for
Mohammed S Hussain
Alireza Ahangar-Asr
You-Liang Chen
Akbar A Javadi
Citations