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

Promotional Information

- 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

Reviews

"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