Bibliothèque de L'IMIST/CNRST

Includes bibliographical references and index.

Genetic algorithms and beyond / Brian T. Luke -- Hybrid genetic algorithms / D. Brynn Hibbert -- Robust soft sensor development using genetic programming / Arthur K. Kordon, Guido F. Smits, Alex N. Kalos, Elsa M. Jordaan -- Genetic algorithms in molecular modelling : a review / Alessandro Maiocchi -- MobyDigs : software for regression and classification models by genetic algorithms / Roberto Todeschini, Viviana Consonni, Andrea Mauri, Manuela Pavan -- Genetic algorithm-PLS as a tool for wavelength selection in spectral data sets / Riccardo Leardi -- Basics of artificial neural networks / Jure Zupan -- Artificial neural networks in molecular structures-property studies / Marjana Novic, Marjan Vracko -- Neural networks for the calibration of voltammetric data / Conrad Bessant, Edward Richards -- Neural networks and genetic algorithms applications in nuclear magnetic resonance spectroscopy / Reinhard Meusinger, Uwe Himmelreich -- A QSAR model for predicting the acute toxicity of pesticides to Gammarids / James Devillers -- Applying genetic algorithms and neural networks to chemometric problems / Brian T. Luke.

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.

Description based on print version record.