| dc.contributor.author |
Barmpalexis, P |
en |
| dc.contributor.author |
Kachrimanis, K |
en |
| dc.contributor.author |
Tsakonas, A |
en |
| dc.contributor.author |
Georgarakis, E |
en |
| dc.date.accessioned |
2014-06-06T06:51:09Z |
|
| dc.date.available |
2014-06-06T06:51:09Z |
|
| dc.date.issued |
2011 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.chemolab.2011.01.012 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/5356 |
|
| dc.subject |
Artificial Neural Network |
en |
| dc.subject |
Controlled Release |
en |
| dc.subject |
Experimental Design |
en |
| dc.subject |
External Validity |
en |
| dc.subject |
Genetic Program |
en |
| dc.subject |
Optimization Model |
en |
| dc.subject |
Pharmaceutical Formulation |
en |
| dc.subject |
Symbolic Regression |
en |
| dc.title |
Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.chemolab.2011.01.012 |
en |
| heal.publicationDate |
2011 |
en |
| heal.abstract |
Symbolic regression via genetic programming (GP) was used in the optimization of a pharmaceutical zero-order release matrix tablet, and its predictive performance was compared to that of artificial neural network (ANN) models. Two types of GP algorithms were employed: 1) standard GP, where a single population is used with a restricted or an extended function set, and 2) multi-population (island |
en |
| heal.journalName |
Chemometrics and Intelligent Laboratory Systems |
en |
| dc.identifier.doi |
10.1016/j.chemolab.2011.01.012 |
en |