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

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

Recenzijas

"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

Papildus informācija

- 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
PREFACE vii
LIST OF CONTRIBUTORS xvii
PART I: GENETIC ALGORITHMS 1(196)
CHAPTER 1 GENETIC ALGORITHMS AND BEYOND
3(52)
(Brian T. Luke)
1 Introduction
3(2)
2 Biological systems and the simple genetic algorithm (SGA)
5(1)
3 Why do GAs work?
6(1)
4 Creating a genetic algorithm
7(21)
4.1 Determining a fitness function
7(1)
4.2 The genetic vector
8(5)
4.3 Creating an initial population
13(1)
4.4 Selection schemes
14(2)
4.5 Mating operators
16(7)
4.6 Mutation operators
23(2)
4.7 Maturation operators
25(1)
4.8 Processing offspring
26(1)
4.9 Termination metrics
27(1)
5 Exploration versus exploitation
28(12)
5.1 The genetic vector
29(1)
5.2 The initial population
30(1)
5.3 Selection schemes
31(2)
5.4 Mating operators
33(1)
5.5 Mutation operators
34(1)
5.6 Maturation operators
34(1)
5.7 Processing offspring
34(2)
5.8 Balancing exploration and exploitation
36(4)
6 Other population-based methods
40(8)
6.1 Parallel GA
41(1)
6.2 Adaptive parallel GA
41(1)
6.3 Meta-GA
42(1)
6.4 Messy GA
42(1)
6.5 Delta coding GA
43(1)
6.6 Tabu search and Gibbs sampling
43(1)
6.7 Evolutionary programming
44(1)
6.8 Evolution strategies
44(1)
6.9 Ant colony optimization
45(1)
6.10 Particle swarm optimization
46(2)
7 Conclusions
48(7)
CHAPTER 2 HYBRID GENETIC ALGORITHMS
55(14)
(D. Brynn Hibbert)
1 Introduction
55(1)
2 The approach to hybridization
55(2)
2.1 Levels of interaction
56(1)
2.2 A simple classification
57(1)
3 Why hybridize?
57(2)
4 Detailed examples
59(7)
4.1 Genetic algorithm with local optimizer
59(3)
4.2 Genetic algorithm-artificial neural network hybrid optimizing quantitative structure-activity relationships
62(1)
4.3 Non-linear partial least squares regression with optimization of the inner relation function by a genetic algorithm
63(1)
4.4 The use of a clustering algorithm in a genetic algorithm
64(2)
5 Conclusion
66(3)
CHAPTER 3 ROBUST SOFT SENSOR DEVELOPMENT USING GENETIC PROGRAMMING
69(40)
(Arthur K. Kordon, Guido F. Smits, Alex N. Kalos, and Elsa M. Jordaan)
1 Introduction
69(2)
2 Soft sensors in industry
71(5)
2.1 Assumptions for soft sensors development
72(1)
2.2 Economic benefits from soft sensors
73(1)
2.3 Soft sensor application areas
74(1)
2.4 Soft sensor vendors
75(1)
3 Requirements for robust soft sensors
76(4)
3.1 Lessons from industrial applications
76(1)
3.2 Design requirements for robust soft sensors
77(3)
4 Selected approaches for effective soft sensors development
80(10)
4.1 Stacked analytical neural networks
80(5)
4.2 Support vector machines
85(5)
5 Genetic programming in soft sensors development
90(9)
5.1 The nature of genetic programming
90(6)
5.2 Solving problems with genetic programming
96(2)
5.3 Advantages of genetic programming in soft sensors development and implementation
98(1)
6 Integrated methodology
99(4)
6.1 Variable selection by analytical neural networks
100(1)
6.2 Data condensation by support vector machines
101(1)
6.3 Inferential model generation by genetic programming
102(1)
6.4 On-line implementation and model self-assessment
102(1)
7 Soft sensor for emission estimation: a case study
103(2)
8 Conclusions
105(4)
CHAPTER 4 GENETIC ALGORITHMS IN MOLECULAR MODELLING: A REVIEW
109(32)
(Alessandro Maiocchi)
1 Introduction
109(1)
2 Molecular modelling and genetic algorithms
110(4)
2.1 How to represent molecular structures and their conformations
111(3)
3 Small and medium-sized molecule conformational search
114(5)
4 Constrained conformational space searches
119(5)
4.1 NMR-derived distance constraints
120(1)
4.2 Pharmacophore-derived constraints
121(1)
4.3 Constrained conformational search by chemical feature superposition
122(2)
5 The protein-ligand docking problem
124(7)
5.1 The scoring functions
126(1)
5.2 Protein-ligand docking with genetic algorithms
127(4)
6 Protein structure prediction with genetic algorithms
131(3)
7 Conclusions
134(7)
CHAPTER 5 MOBYDIGS: SOFTWARE FOR REGRESSION AND CLASSIFICATION MODELS BY GENETIC ALGORITHMS
141(28)
(Roberto Todeschini, Viviana Consonni, Andrea Mauri and Manuela Payan)
1 Introduction
141(2)
2 Population definition
143(1)
3 Tabu list
143(1)
4 Random variables
144(1)
5 Parent selection
145(1)
6 Crossover/mutation trade-off
145(3)
7 Selection pressure and crossover/mutation trade-off influence
148(3)
8 RQK fitness functions
151(3)
9 Evolution of the populations
154(1)
10 Model distance
155(3)
11 The software MobyDigs
158(11)
11.1 The data setup
158(1)
11.2 GA setup
159(2)
11.3 Population evolution view
161(1)
11.4 Modify a single population evolution
162(1)
11.5 Modify multiple population evolution
163(1)
11.6 Analysis of the final models
164(1)
11.7 Variable frequency analysis
165(1)
11.8 Saving results
166(3)
CHAPTER 6 GENETIC ALGORITHM-PLS AS A TOOL FOR WAVELENGTH SELECTION IN SPECTRAL DATA SETS
169(28)
(Riccardo Leardi)
1 Introduction
169(1)
2 The problem of variable selection
170(2)
3 GA applied to variable selection
172(4)
3.1 Initiation of population
172(1)
3.2 Reproduction and mutation
173(1)
3.3 Insertion of new chromosomes
173(1)
3.4 Control of replicates
174(1)
3.5 Influence of the different parameters
174(1)
3.6 Check of subsets
175(1)
3.7 Hybridisation with stepwise selection
176(1)
4 Evolution of the genetic algorithm
176(5)
4.1 The application of randomisation tests
176(1)
4.2 The optimisation of a GA run
177(1)
4.3 Why a single run is not enough
177(1)
4.4 How to take into account the autoconelation among the spectral variables
178(3)
5 Pretreatment and scaling
181(1)
6 Maximum number of variables
182(1)
7 Examples
183(11)
7.1 Data set Soy
183(7)
7.2 Data set Additives
190(4)
8 Conclusions
194(3)
PART II: ARTIFICIAL NEURAL NETWORKS 197(144)
CHAPTER 7 BASICS OF ARTIFICIAL NEURAL NETWORKS
199(32)
(Jure Zupan)
1 Introduction
199(1)
2 Basic concepts
200(4)
2.1 Neuron
200(2)
2.2 Network of neurons
202(2)
3 Error backpropagation ANNs
204(2)
4 Kohonen ANNs
206(7)
4.1 Basic design
206(4)
4.2 Self-organized maps (SOMs)
210(3)
5 Counterpropagation ANNs
213(3)
6 Radial basis function (RBF) networks
216(4)
7 Learning by ANNs
220(3)
8 Applications
223(3)
8.1 Classification
223(1)
8.2 Mapping
224(1)
8.3 Modeling
225(1)
9 Conclusions
226(5)
CHAPTER 8 ARTIFICIAL NEURAL NETWORKS IN MOLECULAR STRUCTURES-PROPERTY STUDIES
231(26)
(Marjana Novic and Marjan Vracko)
1 Introduction
231(1)
2 Molecular descriptors
231(2)
3 Counter propagation neural network
233(4)
3.1 Architecture of a counter propagation neural network
233(2)
3.2 Learning in the Kohonen and output layers
235(1)
3.3 Counter propagation neural network as a tool in QSAR
236(1)
4 Application in toxicology and drug design
237(15)
4.1 A study of aquatic toxicity for the fathead minnow
237(2)
4.2 A study of aquatic toxicity toward Tetrahymena pyriformis on a set of 225 phenols
239(3)
4.3 Example of QSAR modeling with receptor dependent descriptors
242(10)
5 Conclusions
252(5)
CHAPTER 9 NEURAL NETWORKS FOR THE CALIBRATION OF VOLTAMMETRIC DATA
257(24)
(Conrad Bessant and Edward Richards)
1 Introduction
257(1)
2 Electroanalytical data
257(4)
2.1 Amperometry
258(1)
2.2 Pulsed amperometric detection
259(1)
2.3 Voltammetry
259(1)
2.4 Dual pulse staircase voltammetry
259(2)
2.5 Representation of voltammetric data
261(1)
3 Application of artificial neural networks to voltammetric data
261(8)
3.1 Basic approach
262(1)
3.2 Example of ANN calibration of voltammograms
263(6)
3.3 Summary and conclusions
269(1)
4 Genetic algorithms for optimisation of feed forward neural networks
269(9)
4.1 Genes and chromosomes
269(1)
4.2 Choosing parents for the next generation
270(2)
4.3 Results of ANN optimisation by GA
272(5)
4.4 Comparison of optimisation methods
277(1)
5 Conclusions
278(3)
CHAPTER 10 NEURAL NETWORKS AND GENETIC ALGORITHMS APPLICATIONS IN NUCLEAR MAGNETIC RESONANCE (NMR) SPECTROSCOPY
281(42)
(Reinhard Meusinger and Uwe Himmelreich)
1 Introduction
281(2)
2 NMR spectroscopy
283(2)
3 Neural networks applications
285(18)
3.1 Classification
286(4)
3.2 Prediction of properties
290(13)
4 Genetic algorithms
303(6)
4.1 Data processing
304(1)
4.2 Structure determination
305(3)
4.3 Structure prediction
308(1)
4.4 Classification
308(1)
4.5 Feature reduction
309(1)
5 Biomedical NMR spectroscopy
309(6)
6 Conclusion
315(8)
CHAPTER 11 A QSAR MODEL FOR PREDICTING THE ACUTE TOXICITY OF PESTICIDES TO GAMMARIDS
323(18)
(James Devillers)
1 Introduction
323(1)
2 Materials and methods
324(6)
2.1 Toxicity data
324(1)
2.2 Molecular descriptors
324(5)
2.3 Statistical analyses
329(1)
3 Results and discussion
330(8)
3.1 PLS model
330(2)
3.2 ANN model
332(6)
4 Conclusions
338(3)
CONCLUSION 341(36)
CHAPTER 12 APPLYING GENETIC ALGORITHMS AND NEURAL NETWORKS TO CHEMOMETRIC PROBLEMS
343(34)
(Brian T. Luke)
1 Introduction
343(2)
2 Structure of the genetic algorithm
345(5)
3 Results for the genetic algorithms
350(12)
4 Structure of the neural network
362(3)
5 Results for the neural network
365(8)
6 Conclusions
373(4)
INDEX 377


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