Evolutionary Data-Driven Modeling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{Chakraborti:2013:IMSE,
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author = "Nirupam Chakraborti",
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title = "Evolutionary Data-Driven Modeling",
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booktitle = "Informatics for Materials Science and Engineering",
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publisher = "Butterworth-Heinemann",
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year = "2013",
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editor = "Krishna Rajan",
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chapter = "5",
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pages = "71--95",
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address = "Oxford",
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keywords = "genetic algorithms, genetic programming, Neural
network, Multi-objective optimisation, Evolutionary
computation",
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isbn13 = "978-0-12-394399-6",
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DOI = "doi:10.1016/B978-0-12-394399-6.00005-9",
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URL = "http://www.sciencedirect.com/science/article/pii/B9780123943996000059",
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abstract = "Artificial neural networks (ANNs) and genetic
programming (GP) have already emerged as two very
effective computing strategies for constructing
data-driven models for systems of scientific and
engineering interest. However, coming up with accurate
models or meta-models from noisy real-life data is
often a formidable task due to their frequent
association with high degrees of random noise, which
might render an ANN or GP model either over- or
underfitted. This problem has recently been tackled in
two emerging algorithms, Evolutionary Neural Net
(EvoNN) and Bi-objective Genetic Programming (BioGP),
which use Pareto tradeoff and apply a bi-objective
genetic algorithm (GA) in the basic framework of both
ANNs and GP.",
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notes = "Department of Metallurgical and Materials Engineering,
Indian Institute of Technology, Kharagpur, India",
- }
Genetic Programming entries for
Nirupam Chakraborti
Citations