Nature-inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks
- 1st Edition, Volume 23 - December 3, 2003
- Imprint: Elsevier Science
- Editor: Riccardo Leardi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 4 - 5 4 7 7 5 - 0
- Hardback ISBN:9 7 8 - 0 - 4 4 4 - 5 1 3 5 0 - 2
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 5 2 2 6 2 - 3
In recent years Genetic Algorithms (GA) and Artificial Neural Networks (ANN) have progressively increased in importance amongst the techniques routinely used in chemometrics. This… Read more
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Request a sales quoteIn 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.
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.
- 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
Universities, research organisations and private companies world wide, working in the field of Chemometrics, QSAR, data mining, Neural Networks or Genetic Algorithms.
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.
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.
- Edition: 1
- Volume: 23
- Published: December 3, 2003
- Imprint: Elsevier Science
- Language: English
- Paperback ISBN: 9780444547750
- Hardback ISBN: 9780444513502
- eBook ISBN: 9780080522623
RL
Riccardo Leardi
Affiliations and expertise
University of Genova, Genova, ItalyRead Nature-inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks on ScienceDirect