Optimization Techniques for Solving Complex Problems

CONTENTS

CONTRIBUTORS

FOREWORD

PREFACE

PART I   METHODOLOGIES FOR COMPLEX PROBLEM SOLVING

1 Generating Automatic Projections by Means of Genetic Programming

C. Estébanez and R. Aler

1.1 Introduction

1.2 Background

1.3 Domains

1.4 Algorithmic Proposal

1.5 Experimental Analysis

1.6 Conclusions

References

2 Neural Lazy Local Learning

J. M. Valls, I. M. Galván, and P. Isasi

2.1 Introduction

2.2 Lazy Radial Basis Neural Networks

2.3 Experimental Analysis

2.4 Conclusions

References

3 Optimization Using Genetic Algorithms with Micropopulations

Y. Sáez

3.1 Introduction

3.2 Algorithmic Proposal

3.3 Experimental Analysis: The Rastrigin Function

3.4 Conclusions

References

4 Analyzing Parallel Cellular Genetic Algorithms

G. Luque, E. Alba, and B. Dorronsoro

4.1 Introduction

4.2 Cellular Genetic Algorithms

4.3 Parallel Models for cGAs

4.4 Brief Survey of Parallel cGAs

4.5 Experimental Analysis

4.6 Conclusions

References

5 Evaluating New Advanced Multiobjective Metaheuristics

A. J. Nebro, J. J. Durillo, F. Luna, and E. Alba

5.1 Introduction

5.2 Background

5.3 Description of the Metaheuristics

5.4 Experimental Methodology

5.5 Experimental Analysis

5.6 Conclusions

References

6 Canonical Metaheuristics for Dynamic Optimization Problems

G. Leguizamón, G. Ordóñnez, S. Molina, and E. Alba

6.1 Introduction

6.2 Dynamic Optimization Problems

6.3 Canonical MHs for DOPs

6.4 Benchmarks

6.5 Metrics

6.6 ...