Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Giri:2013:ASC,
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author = "Brijesh Kumar Giri and Jussi Hakanen and
Kaisa Miettinen and Nirupam Chakraborti",
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title = "Genetic programming through bi-objective genetic
algorithms with a study of a simulated moving bed
process involving multiple objectives",
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journal = "Applied Soft Computing",
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year = "2013",
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volume = "13",
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number = "5",
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pages = "2613--2623",
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month = may,
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keywords = "genetic algorithms, genetic programming, Evolutionary
algorithms, Neural networks, ANN, Multi-objective
optimisation, MOGP, Computational cost, Meta-models,
Simulation-based optimisation",
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ISSN = "1568-4946",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494612005091",
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DOI = "doi:10.1016/j.asoc.2012.11.025",
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size = "11 pages",
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abstract = "A new bi-objective genetic programming (BioGP)
technique has been developed for meta-modelling and
applied in a chromatographic separation process using a
simulated moving bed (SMB) process. The BioGP technique
initially minimises training error through a single
objective optimisation procedure and then a trade-off
between complexity and accuracy is worked out through a
genetic algorithm based bi-objective optimization
strategy. A benefit of the BioGP approach is that an
expert user or a decision maker (DM) can flexibly
select the mathematical operations involved to
construct a meta-model of desired complexity or
accuracy. It is also designed to combat bloat, a
perennial problem in genetic programming along with
over fitting and under fitting problems. In this study
the meta-models constructed for SMB reactors were
compared with those obtained from an evolutionary
neural network (EvoNN) developed earlier and also with
a polynomial regression model. Both BioGP and EvoNN
were compared for subsequent constrained bi-objective
optimization studies for the SMB reactor involving four
objectives. The results were also compared with the
previous work in the literature. The BioGP technique
produced acceptable results and is now ready for
data-driven modelling and optimization studies at
large.",
- }
Genetic Programming entries for
Brijesh Kumar Giri
Jussi Hakanen
Kaisa Miettinen
Nirupam Chakraborti
Citations