Nature-inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks

Nature-inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks

1st Edition - December 3, 2003

Write a review

  • Editor: Riccardo Leardi
  • eBook ISBN: 9780080522623

Purchase options

Purchase options
DRM-free (PDF)
Sales tax will be calculated at check-out

Institutional Subscription

Free Global Shipping
No minimum order

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.

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

Readership

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

  • 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.

Product details

  • No. of pages: 402
  • Language: English
  • Copyright: © Elsevier Science 2003
  • Published: December 3, 2003
  • Imprint: Elsevier Science
  • eBook ISBN: 9780080522623

About the Editor

Riccardo Leardi

Affiliations and Expertise

University of Genova, Genova, Italy

Ratings and Reviews

Write a review

There are currently no reviews for "Nature-inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks"