Comparison of multi-linear regression, particle swarm optimization artificial neural networks and genetic programming in the development of mini-tablets

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@Article{BARMPALEXIS:2018:IJP,

• author = "Panagiotis Barmpalexis and Anna Karagianni and Grigorios Karasavvaides and Kyriakos Kachrimanis",
• title = "Comparison of multi-linear regression, particle swarm optimization artificial neural networks and genetic programming in the development of mini-tablets",
• journal = "International Journal of Pharmaceutics",
• volume = "551",
• number = "1",
• pages = "166--176",
• year = "2018",
• keywords = "genetic algorithms, genetic programming, Mini-tablets, Quality by design (QbD), Particle swarm optimization ANNs, Flow properties, DoE optimization",
• ISSN = "0378-5173",
• DOI = "doi:10.1016/j.ijpharm.2018.09.026",
• URL = "http://www.sciencedirect.com/science/article/pii/S037851731830677X",
• abstract = "In the present study, the preparation of pharmaceutical mini-tablets was attempted in the framework of Quality by Design (QbD) context, by comparing traditionally used multi-linear regression (MLR), with artificially-intelligence based regression techniques (such as standard artificial neural networks (ANNs), particle swarm optimization (PSO) ANNs and genetic programming (GP)) during Design of Experiment (DoE) implementation. Specifically, the effect of diluent type and particle size fraction for three commonly used direct compression diluents (lactose, pregelatinized starch and dibasic calcium phosphate dihydrate, DCPD) blended with either hydrophilic or hydrophobic flowing aids was evaluated in terms of: a) powder blend properties (such as bulk (Y1) and tapped (Y2) density, Carr's compressibility index (Y3, CCI), Kawakita's compaction fitting parameters a (Y4) and 1/b (Y5)), and b) mini-tablet's properties (such as relative density (Y6), average weight (Y7) and weight variation (Y8)). Results showed better flowing properties for pregelatinized starch and improved packing properties for lactose and DPCD. MLR analysis showed high goodness of fit for the Y1, Y2, Y4, Y6 and Y8 with RMSE values of Y1a =a 0.028, Y2a =a 0.032, Y4a =a 0.019, Y6a =a 0.015 and Y8a =a 0.130; while for rest responses, high correlation was observed from both standard ANNs and GP. PSO-ANNs fitting was the only regression technique that was able to adequately fit all responses simultaneously (RMSE values of Y1a =a 0.026, Y2a =a 0.022, Y3a =a 0.025, Y4a =a 0.010, Y5a =a 0.063, Y6a =a 0.013, Y7a =a 0.064 and Y8a =a 0.104)",

}

Genetic Programming entries for Panagiotis Barmpalexis Anna Karagianni Grigorios Karasavvaides Kyriakos Kachrimanis

Citations