Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
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
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.
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.
- 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.
- No. of pages:
- © Elsevier Science 2003
- 3rd December 2003
- Elsevier Science
- Hardcover ISBN:
- eBook ISBN:
"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." --CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, Vol. 72 (1) 2004
"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
University of Genova, Genova, Italy
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.