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Tables of Contents for Advances in Computational Intelligence
Chapter/Section Title
Page #
Page Count
Introduction
1
14
Ulrich Hammel
Boris Naujoks
Hans--Paul Schwefel
Computational Intelligence Methods
1
2
Parallel Problem Solving from Nature
3
1
On the Development of Computational Intelligence
3
2
State of the Art and Trends in Computational Intelligence
5
1
Goals of the Collaborative Research Center
6
2
On the Organization of the Chapters of This Book
8
7
Part I. Fuzzy Logic
Mathematical Foundations of Fuzzy Inference
15
31
Stephan Lehmke
Bernd Reusch
Karl-Heinz Temme
Helmut Thiele
Introduction
15
1
Structure of Fuzzy Inference Mechanisms
16
2
Interpretation of Fuzzy IF-THEN Rule Bases Using Concepts of Functional Analysis
18
2
The Generalized Modus Ponens and the Compositional Rule of Inference
20
2
Constructing Solutions for Fuzzy IF-THEN Rule Bases
22
6
Uniqueness of Solutions for Fuzzy IF-THEN Rule Bases
28
1
Functional Analytic Properties of Fuzzy Inference Operators
29
1
Approximate Reasoning with Context-Dependent Fuzzy Sets
30
3
Approximate Reasoning Under Uncertainty
33
1
Axiomatic Characterization of Fuzzy Approximation Operators
34
2
Algebraic Foundations of Information Granulation
36
1
Algebraic Foundations of Modeling with Words
37
1
Conclusions and Further Research
37
9
Data-Based Fuzzy Modeling for Complex Applications
46
32
Harro Kiendl
Peter Krause
Daniel Schauten
Timo Slawinski
Introduction
46
2
Efficient Rule Generation in High-dimensional Search Spaces
48
10
Interpretable Fuzzy Models with High Accuracy
58
9
Applications
67
5
Concluding Remarks
72
6
Fuzzy Modeling to Cope with Ambiguities
78
29
Harro Kiendl
Peter Krause
Daniel Schauten
Timo Slawinski
Introduction
78
2
Rating and Reduction of Ambiguities
80
3
Reduction of Ambiguities by Negative Rules
83
4
Resolution of Ambiguities by Defuzzification
87
5
Ambiguity-Based Predesign
92
3
Implicit Modeling
95
12
Part II. Evolutionary Algorithms
Theory of Evolutionary Algorithms and Genetic Programming
107
38
Stefan Droste
Thomas Jansen
Gunter Rudolph
Hans--Paul Schwefel
Karsten Tinnefeld
Ingo Wegener
Introduction
107
2
A Contribution to the NFL Debate
109
2
On the Analysis of Simple Evolutionary Algorithms
111
5
Multiobjective Evolutionary Algorithms
116
3
Populations and Crossover
119
4
Takeover Times and Related Key Measures
123
3
Dynamization and Adaptation
126
4
Metric-Based EA (MBEA) and an Application in GP
130
4
Black-box Optimization
134
11
Design of Evolutionary Algorithms and Applications in Surface Reconstruction
145
49
Thomas Beielstein
Jorn Mehnen
Lutz Schonemann
Hans--Paul Schwefel
Tobias Surmann
Klaus Weinert
Dirk Wiesmann
Introduction
145
1
Design of Evolutionary Algorithms
146
1
Evolutionary Algorithms: The General Framework
146
1
Design of Evolutionary Algorithms and Integration of Domain Knowledge
147
8
Structure Optimization and Evolutionary Programming
155
6
An Analysis of Threshold Selection in Noisy Environments
161
4
Evolutionary Surface Reconstruction
165
2
Scanning Techniques
167
1
Point Selection Schemes
168
4
Surface Reconstruction Using Triangulations
172
6
Surface Reconstruction Using Nonuniform Rational B-Splines
178
4
A Parallel Reconstruction System Using CSG-NURBS Hybrids
182
3
Parallel Evolution Strategies
185
2
Conclusions
187
7
Genetic Programming and Its Application in Machining Technology
194
51
Wolfgang Banzhaf
Markus Brameier
Marc Stautner
Klaus Weinert
Introduction
194
1
Linear Genetic Programming
195
2
Removal of Non-effective Code
197
2
Graph Interpretation
199
2
Linear Genetic Operators
201
6
Control of Variation Step Size
207
5
Control of Structural Diversity
212
6
Genetic Programming in Machining Technology
218
1
Experimental Setup
219
2
Gathering Data
221
3
Tree-Based GP for Symbolic Regression
224
5
Graphical Representation
229
2
Results of the GP Kernel
231
4
Parallelization
235
2
Conclusion
237
8
Part III. Machine Learning and Optimization
Novel Learning Tasks, Optimization, and Their Application
245
74
Guido Daniel
Jan Dienstuhl
Sebastian Engell
Sven Felske
Karl Goser
Ralf Klinkenberg
Katharina Morik
Oliver Ritthoff
Henner Schmidt--Traub
Novel Learning Tasks in Machine Learning
246
32
Parameter Estimation for Chromatographic Processes
278
16
Classification and Prediction of Device Parameters with Regard to the Quality of Integrated Microelectronic Systems
294
25
Index
319