Nature-inspired methods in chemometrics: genetic algorithms and artificial neural networks, Volume 23

1st Edition

Editors: Riccardo Leardi
Print ISBN: 9780444547750
eBook ISBN: 9780080522623
Imprint: Elsevier Science
Published Date: 3rd December 2003
Page Count: 402
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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.

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


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

Table of Contents


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, 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


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 M


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Elsevier Science
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About the Editor

Riccardo Leardi

Affiliations and Expertise

University of Genova, Genova, Italy


@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