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Nature insprired Methods in Chemometrics Genetic Algorithms and Artificial Neural Networks 1st Edition by R Leardi ISBN 0444513507 9780444513502

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Nature insprired Methods in Chemometrics Genetic Algorithms and Artificial Neural Networks 1st Edition R. Leardi (Eds.) Digital Instant Download

Author(s): R. Leardi (Eds.)
ISBN(s): 9780444513502, 0444513507
Edition: 1
File Details: PDF, 4.30 MB
Year: 2003
Language: english
SKU: EB-1294584 Category: Tag:

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ISBN 10: 0444513507 
ISBN 13: 9780444513502
Author: R Leardi

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

Nature insprired Methods in Chemometrics Genetic Algorithms and Artificial Neural Networks 1st Table of contents:

PART I: Genetic Algorithms

CHAPTER 1. Genetic Algorithms and Beyond

  1. Introduction

  2. Biological systems and the simple genetic algorithm (SGA)

  3. Why do GAs work?

  4. Creating a genetic algorithm

  5. Exploration versus exploitation

  6. Other population-based methods

  7. Conclusions

CHAPTER 2. Hybrid Genetic Algorithms

  1. Introduction

  2. The approach to hybridization

  3. Why hybridize?

  4. Detailed examples

  5. Conclusion

CHAPTER 3. Robust Soft Sensor Development Using Genetic Programming

  1. Introduction

  2. Soft sensors in industry

  3. Requirements for robust soft sensors

  4. Selected approaches for effective soft sensors development

  5. Genetic programming in soft sensors development

  6. Integrated methodology

  7. Soft sensor for emission estimation: a case study

  8. Conclusions

CHAPTER 4. Genetic Algorithms in Molecular Modelling: A Review

  1. Introduction

  2. Molecular modelling and genetic algorithms

  3. Small and medium-sized molecule conformational search

  4. Constrained conformational space searches

  5. The protein-ligand docking problem

  6. Protein structure prediction with genetic algorithms

  7. Conclusions

CHAPTER 5. Mobydigs: Software for Regression and Classification Models by Genetic Algorithms

  1. Introduction

  2. Population definition

  3. Tabu list

  4. Random variables

  5. Parent selection

  6. Crossover/mutation trade-off

  7. Selection pressure and crossover/mutation trade-off influence

  8. RQK fitness functions

  9. Evolution of the populations

  10. Model distance

  11. The software MobyDigs

CHAPTER 6. Genetic Algorithm‑PLS as a Tool for Wavelength Selection in Spectral Data Sets

  1. Introduction

  2. The problem of variable selection

  3. GA applied to variable selection

  4. Evolution of the genetic algorithm

  5. Pretreatment and scaling

  6. Maximum number of variables

  7. Examples

  8. Conclusions

PART II: Artificial Neural Networks

CHAPTER 7. Basics of Artificial Neural Networks

  1. Introduction

  2. Basic concepts

  3. Error backpropagation ANNs

  4. Kohonen ANNs

  5. Counterpropagation ANNs

  6. Radial basis function (RBF) networks

  7. Learning by ANNs

  8. Applications

  9. Conclusions

CHAPTER 8. Artificial Neural Networks in Molecular Structures–Property Studies

  1. Introduction

  2. Molecular descriptors

  3. Counter propagation neural network

  4. Application in toxicology and drug design

  5. Conclusions

CHAPTER 9. Neural Networks for the Calibration of Voltammetric Data

  1. Introduction

  2. Electroanalytical data

  3. Application of artificial neural networks to voltammetric data

  4. Genetic algorithms for optimisation of feed-forward neural networks

  5. Conclusions

CHAPTER 10. Neural Networks and Genetic Algorithms Applications in Nuclear Magnetic Resonance (NMR)

  1. Introduction

  2. NMR spectroscopy

  3. Neural networks applications

  4. Genetic algorithms

  5. Biomedical NMR spectroscopy

  6. Conclusion

CHAPTER 11. A QSAR Model for Predicting the Acute Toxicity of Pesticides to Gammarids

  1. Introduction

  2. Materials and methods

  3. Results and discussion

  4. Conclusions

Conclusion

CHAPTER 12. Applying Genetic Algorithms and Neural Networks to Chemometric Problems

  1. Introduction

  2. Structure of the genetic algorithm

  3. Results for the genetic algorithms

  4. Structure of the neural network

  5. Results for the neural network

  6. Conclusions

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Tags: R Leardi, Nature insprired, Chemometrics Genetic