Application of Genetic Programming Models Incorporated in Optimization Models for Contaminated Groundwater Systems Management
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Datta:2014:EVOLVE,
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author = "Bithin Datta and Om Prakash and
Janardhanan Sreekanth",
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title = "Application of Genetic Programming Models Incorporated
in Optimization Models for Contaminated Groundwater
Systems Management",
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booktitle = "EVOLVE - A Bridge between Probability, Set Oriented
Numerics, and Evolutionary Computation V",
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year = "2014",
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editor = "Alexandru-Adrian Tantar and Emilia Tantar and
Jian-Qiao Sun and Wei Zhang and Qian Ding and
Oliver Schuetze and Michael Emmerich and Pierrick Legrand and
Pierre {Del Moral} and Carlos A. {Coello Coello}",
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volume = "288",
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series = "Advances in Intelligent Systems and Computing",
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pages = "183--199",
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address = "Peking",
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month = "1-4 " # jul,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Optimal
Monitoring Network, Groundwater Pollution,
Multi-Objective Optimisation, Pollution Source
Identification, Simulated Annealing, Impact Factors,
Ensemble Surrogates",
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isbn13 = "978-3-319-07493-1",
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DOI = "doi:10.1007/978-3-319-07494-8_13",
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abstract = "Two different applications of Genetic Programming (GP)
for solving large scale groundwater management problems
are presented here. Efficient groundwater contamination
management needs solution of large sale simulation
models as well as solution of complex optimal decision
models. Often the best approach is to use linked
simulation optimisation models. However, the
integration of optimisation algorithm with large scale
simulation of the physical processes, which require
very large number of iterations, impose enormous
computational burden. Often typical solutions need
weeks of computer time. Suitably trained GP based
surrogate models approximating the physical processes
can improve the computational efficiency enormously,
also ensuring reasonably accurate solutions. Also, the
impact factors obtained from the GP models can help in
the design of monitoring networks under uncertainties.
Applications of GP for obtaining impact factors
implicitly based on a surrogate GP model, showing the
importance of a chosen monitoring location relative to
a potential contaminant source is also presented. The
first application uses GP models based impact factors
for optimal design of monitoring networks for efficient
identification of unknown contaminant sources. The
second application uses GP based ensemble surrogate
models within a linked simulation optimisation model
for optimal management of saltwater intrusion in
coastal aquifers.",
- }
Genetic Programming entries for
Bithin Datta
Om Prakash
Janardhanan Sreekanth
Citations