Modelling formulations using gene expression programming - A comparative analysis with artificial neural networks

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@Article{Colbourn2011366,

• author = "E. A. Colbourn and S. J. Roskilly and R. C. Rowe and P. York",
• title = "Modelling formulations using gene expression programming - A comparative analysis with artificial neural networks",
• journal = "European Journal of Pharmaceutical Sciences",
• volume = "44",
• number = "3",
• pages = "366--374",
• year = "2011",
• ISSN = "0928-0987",
• DOI = "doi:10.1016/j.ejps.2011.08.021",
• URL = "http://www.sciencedirect.com/science/article/pii/S0928098711002958",
• keywords = "genetic algorithms, genetic programming, gene expression programming, Neural networks, Modelling, Formulation",
• 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

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