Optimization Techniques for Solving Complex Problems

Real-world problems and modern optimization techniques to solve
them

Here, a team of international experts brings together core ideas
for solving complex problems in optimization across a wide variety
of real-world settings, including computer science, engineering,
transportation, telecommunications, and bioinformatics.

Part One--covers methodologies for complex problem solving
including genetic programming, neural networks, genetic algorithms,
hybrid evolutionary algorithms, and more.

Part Two--delves into applications including DNA sequencing
and reconstruction, location of antennae in telecommunication
networks, metaheuristics, FPGAs, problems arising in
telecommunication networks, image processing, time series
prediction, and more.

All chapters contain examples that illustrate the applications
themselves as well as the actual performance of the
algorithms.?Optimization Techniques for Solving Complex Problems is
a valuable resource for practitioners and researchers who work with
optimization in real-world settings.

Autorentext

ENRIQUE ALBA is a Professor of Data Communications and Evolutionary Algorithms at the University of Málaga, Spain. CHRISTIAN BLUM is a Research Fellow at the ALBCOM research group of the Universitat Politècnica de Catalunya, Spain. PEDRO ISASI??is a Professor of Artificial Intelligence at the University Carlos III of Madrid, Spain. COROMOTO LEÓN is a Professor of Language Processors and Distributed Programming at the University of La Laguna, Spain. JUAN ANTONIO??GÓMEZ is a Professor of Computer Architecture and Reconfigurable Computing at the University of Extremadura, Spain.??

Zusammenfassung
Real-world problems and modern optimization techniques to solve them Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics.

Part Onecovers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more.

Part Twodelves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more.

All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings.

Inhalt

Contributors xv

Foreword xix

Preface xxi

Part I Methodologies for Complex Problem Solving 1

**1 Generating Automatic Projections by Means of Genetic Programming 3
**C. Estébanez and R. Aler

1.1 Introduction 3

1.2 Background 4

1.3 Domains 6

1.4 Algorithmic Proposal 6

1.5 Experimental Analysis 9

1.6 Conclusions 11

References 13

**2 Neural Lazy Local Learning 15
**J. M. Valls, I. M. Galván, and P. Isasi

2.1 Introduction 15

2.2 Lazy Radial Basis Neural Networks 17

2.3 Experimental Analysis 22

2.4 Conclusions 28

References 30

**3 Optimization Using Genetic Algorithms with Micropopulations 31
**Y. Sáez

3.1 Introduction 31

3.2 Algorithmic Proposal 33

3.3 Experimental Analysis: The Rastrigin Function 40

3.4 Conclusions 44

References 45

**4 Analyzing Parallel Cellular Genetic Algorithms 49
**G. Luque, E. Alba, and B. Dorronsoro

4.1 Introduction 49

4.2 Cellular Genetic Algorithms 50

4.3 Parallel Models for cGAs 51

4.4 Brief Survey of Parallel cGAs 52

4.5 Experimental Analysis 55

4.6 Conclusions 59

References 59

**5 Evaluating New Advanced Multiobjective Metaheuristics 63
**A. J. Nebro, J. J. Durillo, F. Luna, and E. Alba

5.1 Introduction 63

5.2 Background 65

5.3 Description of the Metaheuristics 67

5.4 Experimental Methodology 69

5.5 Experimental Analysis 72

5.6 Conclusions 79

References 80

**6 Canonical Metaheuristics for Dynamic Optimization Problems 83
*G. Leguizamón, G. Ordóñez, S. Molina, and E. Alba*

6.1 Introduction 83

6.2 Dynamic Optimization Problems 84

6.3 Canonical MHs for DOPs 88

6.4 Benchmarks 92

6.5 Metrics 93

6.6 Conclusions 95

References 96

**7 Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms 101
**C. Cotta and A. J. Fernández

7.1 Introduction 101

7.2 Strategies for Solving CCOPs with HEAs 103

7.3 Study Cases 105

7.4 Conclusions 114

References 115

**8 Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques 123
*J. A. Gómez, M. D. Jaraiz, M. A. Vega, and J. M. Sánchez*

8.1 Introduction 123

8.2 Time Series Identification 124

8.3 Optimization Problem 125

8.4 Algorithmic Proposal 130

8.5 Experimental Analysis 132

8.6 Conclusions 136

References 136

**9 Using Reconfigurable Computing for the Optimization of Cryptographic Algorithms 139
*J. M. Granado, M. A. Vega, J. M. Sánchez, and J. A. Gómez*

9.1 Introduction 139

9.2 Description of the Cryptographic Algorithms 140

9.3 Implementation Proposal 144

9.4 Expermental Analysis 153

9.5 Conclusions 154

References 155

**10 Genetic Algorithms, Parallelism, and Reconfigurable Hardware 159
*J. M. Sánchez, M. Rubio, M. A. Vega, and J. A. Gómez*

10.1 Introduction 159

10.2 State of the Art 161

10.3 FPGA Problem Description and Solution 162

10.4 Algorithmic Proposal 169

10.5 Experimental Analysis 172

10.6 Conclusions 177

References 177

**11 Divide and Conquer: Advanced Techniques 179
*C. León, G. Miranda, and C. Rodríguez* 1...