Genetic Programming for Modeling Vibratory Finishing Process: Role of Experimental Designs and Fitness Functions
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{conf/semcco/GargT13,
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author = "Akhil Garg and Kang Tai",
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title = "Genetic Programming for Modeling Vibratory Finishing
Process: Role of Experimental Designs and Fitness
Functions",
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booktitle = "Proceedings of the 4th International Conference on
Swarm, Evolutionary, and Memetic Computing (SEMCCO
2013), Part II",
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year = "2013",
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editor = "Bijaya Ketan Panigrahi and
Ponnuthurai Nagaratnam Suganthan and Swagatam Das and Subhransu Sekhar Dash",
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volume = "8298",
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series = "Lecture Notes in Computer Science",
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pages = "23--31",
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address = "Chennai, India",
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month = dec # " 19-21",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, vibratory
finishing, fitness function, vibratory modelling,
GPTIPS, experimental designs, finishing modelling",
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isbn13 = "978-3-319-03755-4",
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bibdate = "2013-12-18",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/semcco/semcco2013-2.html#GargT13",
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URL = "http://dx.doi.org/10.1007/978-3-319-03756-1",
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DOI = "doi:10.1007/978-3-319-03756-1_3",
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abstract = "Manufacturers seek to improve efficiency of vibratory
finishing process while meeting increasingly stringent
cost and product requirements. To serve this purpose,
mathematical models have been formulated using soft
computing methods such as artificial neural network and
genetic programming (GP). Among these methods, GP
evolves model structure and its coefficients
automatically. There is extensive literature on ways to
improve the performance of GP but less attention has
been paid to the selection of appropriate experimental
designs and fitness functions. The evolution of fitter
models depends on the experimental design used to
sample the problem (system) domain, as well as on the
appropriate fitness function used for improving the
evolutionary search. This paper presents quantitative
analysis of two experimental designs and four fitness
functions used in GP for the modelling of vibratory
finishing process. The results conclude that fitness
function SRM and PRESS evolves GP models of higher
generalisation ability, which may then be deployed by
experts for optimisation of the finishing process.",
- }
Genetic Programming entries for
Akhil Garg
Kang Tai
Citations