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NATURE-INSPIRED METHODS IN CHEMOMETRICS: GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORKS, 23
Nature-inspired methods in chemometrics: genetic algorithms and artificial neural networks, 23To order this title, and for more information, click here

Edited By
Riccardo Leardi, University of Genova, Genova, Italy

Included in series
Data Handling in Science and Technology, 23

Description
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.

Audience
Universities, research organisations and private companies world wide, working in the field of Chemometrics, QSAR, data mining, Neural Networks or Genetic Algorithms.

Contents
PART I: GENETIC ALGORITHMS Chapter 1: Genetic Algorithms and Beyond Brian T. Luke SAIC-Frederick, Inc., Advanced Biomedical Computing Center, NCI Frederick, P.O. Box B, Frederick, MD 21702, USA Chapter 2: Hybrid Genetic Algorithms D. Brynn Hibbert School of Chemical Sciences, University of New South Wales, Sydney, NSW2052, Australia Chapter 3: Robust Soft Sensor Development Using Genetic Programming Arthur K. Kordona , Guido F. Smits,b Alex N. Kalosa, and Elsa M. Jordaan b aThe Dow Chemical Company, Freeport, TX 77566, USA bDow Benelux NV, Terneuzen, The Netherlands Chapter 4: Genetic Algorithms in Molecular Modeling: a Review Alessandro Maiocchi Bracco Imaging S.p.A., Milano Research Center, via E. Folli 50, 20134 Milano, Italy Chapter 5: MobyDigs: Sofwtare for Regression and Classification Models by Genetic Algorithms. Roberto Todeschini, Viviana Consonni, Andrea Mauri and Manuela Pavan Milano Chemometrics and QSAR Research Group, Dept. of Environmental Sciences, P.za della Scienza, 1, 20126 Milano, Italy Chapter 6: Genetic Algorithm-PLS as a tool for wavelength selection in spectral data sets Riccardo Leardi University of Genova, Dept. of Pharmaceutical and Food Chemistry and Technology, via Brigata Salerno (ponte), 16147 Genova, Italy PART II: ARTIFICIAL NEURAL NETWORKS Chapter 7: Basics of Artificial Neural Networks Jure Zupan Laboratory of Chemometrics, National Institute of Chemistry, Ljubljana, Slovenia Chapter 8: Artificial Neural Networks in Molecular Structures-Property Studies Marjana Novic and Marjan Vracko Laboratory of Chemometrics, National Institute of Chemistry, Ljubljana, Slovenia Chapter 9: Neural Networks for the Calibration of Voltammetric Data Conrad Bessant and Edward Richards Cranfield Centre for Analytical Science, Cranfield University, Silsoe, Bedford MK45 4DT. UK. Chapter 10: Neural Networks and Genetic Algorithms Applications in Nuclear Magnetic Resonance (NMR) Spectroscopy Reinhard Meusingera and Uwe Himmelreichb aTechnical University of Darmstadt, Institute of Organic Chemistry, Petersenstrasse 22, D-64287 Darmstadt, Germany bUniversity of Sidney, Institute of Magnetic Resonance Research, Blackburn Bldg D06, Sydney, NSW 2006, Australia Chapter 11: A QSAR Model for Predicting the Acute Toxicity of Pesticides to Gammarids James Devillers CTIS, 3 Chemin de la Graviere, 69140 Rillieux La Pape, France CONCLUSION Chapter 12: Applying Genetic Algorithms and Neural Networks to Chemometric Problems Brian T. Luke SAIC-Frederick, Inc., Advanced Biomedical Computing Center, NCI Frederick, P.O. Box B, Frederick, MD 21702, USA.

Bibliographic details
Hardbound, 402 pages, publication date: DEC-2003
ISBN-13: 978-0-444-51350-2
ISBN-10: 0-444-51350-7
Imprint: ELSEVIER

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Last update: 27 Sep 2008
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