Nature-inspired methods in chemometrics: genetic algorithms and artificial neural networks

Edited by

  • Riccardo Leardi, University of Genova, Genova, Italy

In recent years Genetic Algorithms (GA) and Artificial Neural Networks (ANN) have progressively increased in importance amongst the techniques routinely used in chemometrics. This book contains contributions from experts in the field is divided in two sections (GA and ANN). In each part, tutorial chapters are included in which the theoretical bases of each technique are expertly (but simply) described. These are followed by application chapters in which special emphasis will be given to the advantages of the application of GA or ANN to that specific problem, compared to classical techniques, and to the risks connected with its misuse. This book is of use to all those who are using or are interested in GA and ANN. Beginners can focus their attentions on the tutorials, whilst the most advanced readers will be more interested in looking at the applications of the techniques. It is also suitable as a reference book for students.
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Universities, research organisations and private companies world wide, working in the field of Chemometrics, QSAR, data mining, Neural Networks or Genetic Algorithms.


Book information

  • Published: December 2003
  • Imprint: ELSEVIER
  • ISBN: 978-0-444-51350-2


"This book serves as a useful reference and twenty-third volume to the Data Handling in Science and Technology series."
Peter De. B. Harrington, Ohio University, Ohio, APPLIED SPECTROSCOPY, Vol. 59, No. 4, 2005

"Overall, the reader is given an excellent introduction to GAs and their use in conjunction with other methods applied to several important problems. The applications chapters provide interesting examples and much information on how to configure GAs and ANNs.
"Each part begins with a chapter that provides an excellent introduction to the technique. For persons who are involved in chemistry modeling, this would be a good book to own."
TECHNOMETRICS, Vol. 47, No. 1, 2005

Table of Contents

PART I: GENETIC ALGORITHMSChapter 1: Genetic Algorithms and BeyondBrian T. LukeSAIC-Frederick, Inc., Advanced Biomedical Computing Center, NCI Frederick, P.O. Box B, Frederick, MD 21702, USAChapter 2: Hybrid Genetic AlgorithmsD. Brynn HibbertSchool of Chemical Sciences, University of New South Wales, Sydney, NSW2052, AustraliaChapter 3: Robust Soft Sensor Development Using Genetic ProgrammingArthur K. Kordona , Guido F. Smits,b Alex N. Kalosa, and Elsa M. Jordaan baThe Dow Chemical Company, Freeport, TX 77566, USAbDow Benelux NV, Terneuzen, The NetherlandsChapter 4: Genetic Algorithms in Molecular Modeling: a ReviewAlessandro MaiocchiBracco Imaging S.p.A., Milano Research Center, via E. Folli 50, 20134 Milano, ItalyChapter 5: MobyDigs: Sofwtare for Regression and Classification Models by Genetic Algorithms.Roberto Todeschini, Viviana Consonni, Andrea Mauri and Manuela PavanMilano Chemometrics and QSAR Research Group, Dept. of Environmental Sciences, della Scienza, 1, 20126 Milano, ItalyChapter 6: Genetic Algorithm-PLS as a tool for wavelength selection in spectral data setsRiccardo LeardiUniversity of Genova, Dept. of Pharmaceutical and Food Chemistry and Technology, via Brigata Salerno (ponte), 16147 Genova, ItalyPART II: ARTIFICIAL NEURAL NETWORKSChapter 7: Basics of Artificial Neural Networks Jure ZupanLaboratory of Chemometrics, National Institute of Chemistry, Ljubljana, Slovenia Chapter 8: Artificial Neural Networks in Molecular Structures-Property StudiesMarjana Novic and Marjan VrackoLaboratory of Chemometrics, National Institute of Chemistry, Ljubljana, Slovenia Chapter 9: Neural Networks for the Calibration of Voltammetric DataConrad Bessant and Edward RichardsCranfield Centre for Analytical Science, Cranfield University, Silsoe, Bedford MK45 4DT. UK.Chapter 10: Neural Networks and Genetic Algorithms Applications in Nuclear Magnetic Resonance (NMR) SpectroscopyReinhard Meusingera and Uwe HimmelreichbaTechnical University of Darmstadt, Institute of Organic Chemistry, Petersenstrasse 22, D-64287 Darmstadt, GermanybUniversity of Sidney, Institute of Magnetic Resonance Research, Blackburn Bldg D06, Sydney, NSW 2006, AustraliaChapter 11: A QSAR Model for Predicting the Acute Toxicity of Pesticides to GammaridsJames DevillersCTIS, 3 Chemin de la Gravière, 69140 Rillieux La Pape, FranceCONCLUSIONChapter 12: Applying Genetic Algorithms and Neural Networks to Chemometric ProblemsBrian T. LukeSAIC-Frederick, Inc., Advanced Biomedical Computing Center, NCI Frederick, P.O. Box B, Frederick, MD 21702, USA.