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

Nature-inspired methods in chemometrics: genetic algorithms and artificial neural networks, 1st Edition

 
Nature-inspired methods in chemometrics: genetic algorithms and artificial neural networks, 1st Edition,Riccardo Leardi,ISBN9780444513502
 
 
 

R Leardi   

Elsevier Science

9780444513502

9780080522623

402

240 X 165

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Key Features

- Subject matter is steadily increasing in importance
- Comparison of Genetic Algorithms (GA) and Artificial Neural Networks (ANN) with the classical techniques
- Suitable for both beginners and advanced researchers

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.

Readership

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

Riccardo Leardi

Affiliations and Expertise

University of Genova, Genova, Italy

Nature-inspired methods in chemometrics: genetic algorithms and artificial neural networks, 1st Edition

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 Gravière, 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.

Quotes and reviews

@qu: "This book serves as a useful reference and twenty-third volume to the Data Handling in Science and Technology series."

@source: Peter De. B. Harrington, Ohio University, Ohio, APPLIED SPECTROSCOPY, Vol. 59, No. 4, 2005

@qu: "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.

@source: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, Vol. 72 (1) 2004

@qu: "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."

@source: TECHNOMETRICS, Vol. 47, No. 1, 2005
 
 
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