Created by W.Langdon from gp-bibliography.bib Revision:1.5776

- @InProceedings{conf/wilf/Floares05,
- title = "Genetic Programming and Neural Networks Feedback Linearization for Modeling and Controlling Complex Pharmacogenomic Systems",
- author = "Alexandru Floares",
- year = "2005",
- editor = "Isabelle Bloch and Alfredo Petrosino and Andrea Tettamanzi",
- publisher = "Springer",
- series = "Lecture Notes in Computer Science",
- volume = "3849",
- booktitle = "Fuzzy Logic and Applications, 6th International Workshop, WILF 2005, Revised Selected Papers",
- pages = "178--187",
- address = "Crema, Italy",
- month = sep # " 15-17",
- bibdate = "2006-02-22",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/wilf/wilf2005.html#Floares05",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "3-540-32529-8",
- DOI = "doi:10.1007/11676935_22",
- abstract = "Modern pharmacology, combining pharmacokinetic, pharmacodynamic, and pharmacogenomic data, is dealing with high dimensional, nonlinear, stiff systems. Mathematical modelling of these systems is very difficult, but important for understanding them. At least as important is to adequately control them through inputs - drugs' dosage regimes. Genetic programming (GP) and neural networks (NN) are alternative techniques for these tasks. We use GP to automatically write the model structure in C++ and optimise the model's constants. This gives insights into the subjacent molecular mechanisms. We also show that NN feedback linearisation (FBL) can adequately control these systems, with or without a mathematical model. The drug dosage regimen will determine the output of the system to track very well a therapeutic objective. To our knowledge, this is the first time when a very large class of complex pharmacological problems are formulated and solved in terms of GP modeling and NN modeling and control.",
- notes = "Published 2006?",
- }

Genetic Programming entries for Alexandru Floares