Modelling formulations using gene expression programming - A comparative analysis with artificial neural networks
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- @Article{Colbourn2011366,
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author = "E. A. Colbourn and S. J. Roskilly and R. C. Rowe and
P. York",
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title = "Modelling formulations using gene expression
programming - A comparative analysis with artificial
neural networks",
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journal = "European Journal of Pharmaceutical Sciences",
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volume = "44",
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number = "3",
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pages = "366--374",
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year = "2011",
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ISSN = "0928-0987",
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DOI = "doi:10.1016/j.ejps.2011.08.021",
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URL = "http://www.sciencedirect.com/science/article/pii/S0928098711002958",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, Neural networks, Modelling,
Formulation",
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abstract = "This study has investigated the utility and potential
advantages of gene expression programming (GEP) - a new
development in evolutionary computing for modelling
data and automatically generating equations that
describe the cause-and-effect relationships in a
system- to four types of pharmaceutical formulation and
compared the models with those generated by neural
networks, a technique now widely used in the
formulation development. Both methods were capable of
discovering subtle and non-linear relationships within
the data, with no requirement from the user to specify
the functional forms that should be used. Although the
neural networks rapidly developed models with higher
values for the ANOVA R2 these were black box and
provided little insight into the key relationships.
However, GEP, although significantly slower at
developing models, generated relatively simple
equations describing the relationships that could be
interpreted directly. The results indicate that GEP can
be considered an effective and efficient modelling
technique for formulation data.",
- }
Genetic Programming entries for
E A Colbourn
S J Roskilly
Raymond C Rowe
Peter York
Citations