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

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Overview

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

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

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Editorial Reviews

From the Publisher
"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

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Product Details

Table of Contents

PART I: GENETIC ALGORITHMS
Chapter 1: Genetic Algorithms and Beyond
Chapter 2: Hybrid Genetic Algorithms
Chapter 3: Robust Soft Sensor Development Using Genetic Programming
Chapter 4: Genetic Algorithms in Molecular Modeling: a Review
Chapter 5: MobyDigs: Sofwtare for Regression and Classification Models by Genetic Algorithms.
Chapter 6: Genetic Algorithm-PLS as a tool for wavelength selection in spectral data sets
PART II: ARTIFICIAL NEURAL NETWORKS
Chapter 7: Basics of Artificial Neural Networks
Chapter 8: Artificial Neural Networks in Molecular Structures-Property Studies
Chapter 9: Neural Networks for the Calibration of Voltammetric Data
Chapter 10: Neural Networks and Genetic Algorithms Applications in Nuclear Magnetic Resonance (NMR) Spectroscopy
Chapter 11: A QSAR Model for Predicting the Acute Toxicity of Pesticides to Gammarids
CONCLUSION
Chapter 12: Applying Genetic Algorithms and Neural Networks to Chemometric Problems
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