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 Conclusions

References

7 Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms

C. Cotta and A. J. Fernández

7.1 Introduction

7.2 Strategies for Solving CCOPs with HEAs

7.3 Study Cases

7.4 Conclusions

References

8 Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques

J. A. Gómez, M. D. Jaraiz, M. A. Vega, and J. M. Sánchez

8.1 Introduction

8.2 Time Series Identification

8.3 Optimization Problem

8.4 Algorithmic Proposal

8.5 Experimental Analysis

8.6 Conclusions

References

9 Using Reconfigurable Computing for the Optimization of Cryptographic Algorithms

J. M. Granado, M. A. Vega, J. M. Sánchez, and J. A. Gómez

9.1 Introduction

9.2 Description of the Cryptographic Algorithms

9.3 Implementation Proposal

9.4 Expermental Analysis

9.5 Conclusions

References

10 Genetic Algorithms, Parallelism, and Reconfigurable Hardware

J. M. Sánchez, M. Rubio, M. A. Vega, and J. A. Gómez

10.1 Introduction

10.2 State of the Art

10.3 FPGA Problem Description and Solution

10.4 Algorithmic Proposal

10.5 Experimental Analysis

10.6 Conclusions

References

11 Divide and Conquer: Advanced Techniques

C. León, G. Miranda, and C. Rodríguez

11.1 Introduction

11.2 Algorithm of the Skeleton

11.3 Experimental Analysis

11.4 Conclusions

References

12 Tools for Tree Searches: Branch-and-Bound and A* Algorithms

C. León, G. Miranda, and C. Rodríguez

12.1 Introduction

12.2 Background

12.3 Algorithmic Skeleton for Tree Searches

12.4 Experimentation Methodology

12.5 Experimental Results

12.6 Conclusions

References

13 Tools for Tree Searches: Dynamic Programming

C. León, G. Miranda, and C. Rodríguez

13.1 Introduction

13.2 Top-Down Approach

13.3 Bottom-Up Approach

13.4 Automata Theory and Dynamic Programming

13.5 Parallel Algorithms

13.6 Dynamic Programming Heuristics

13.7 Conclusions

References

PART II APPLICATIONS

14 Automatic Search of Behavior Strategies in Auctions

D. Quintana and A. Mochón

14.1 Introduction

14.2 Evolutionary Techniques in Auctions

14.3 Theoretical Framework: The Ausubel Auction

14.4 Algorithmic Proposal

14.5 Experimental Analysis

14.6 Conclusions

References

15 Evolving Rules for Local Time Series Prediction

C. Luque, J. M. Valls, and P. Isasi

15.1 Introduction

15.2 Evolutionary Algorithms for Generating Prediction Rules

15.3 Experimental Methodology

15.4 Experiments

15.5 Conclusions

References

16 Metaheuristics in Bioinformatics: DNA Sequencing and Reconstruction

C. Cotta, A. J. Fernández, J. E. Gallardo, G. Luque, and E. Alba

16.1 Introduction

16.2 Metaheuristics and Bioinformatics

16.3 DNA Fragment Assembly Problem

16.4 Shortest Common Supersequence Problem

16.5 Conclusions

References

17 Optimal Location of Antennas in Telecommunication Networks

G. Molina, F. Chicano, and E. Alba

17.1 Introduction

17.2 State of the Art

17.3 Radio Network Design Problem

17.4 Optimization Algorithms

17.5 Basic Problems

17.6 Advanced Problem

17.7 Conclusions

References

18 Optimization of Image-Processing Algorithms Using FPGAs

M. A. Vega, A. Gómez, J. A. Gómez, and J. M. Sánchez

18.1 Introduction

18.2 Background

18.3 Main Features of FPGA-Based Image Processing

18.4 Advanced Details

18.5 Experimental Analysis: Software Versus FPGA

18.6 Conclusions

References

19 Application of Cellular Automata Algorithms to the Parallel Simulation of Laser Dynamics

J. L. Guisado, F. Jiménez-Morales, J. M. Guerra, and F. Fernández

19.1 Introduction

19.2 Background

19.3 Laser Dynamics Problem

19.4 Algorithmic Proposal

19.5 Experimental Analysis

19.6 Parallel Implementation of the Algorithm

19.7 Conclusions

References

20 Dense Stereo Disparity from an Artificial Life Standpoint

G. Olague, F. Fernández, C. B. Pérez, and E. Lutton

20.1 Introduction

20.2 Infection Algorithm with an Evolutionary Approach

20.3 Experimental Analysis

20.4 Conclusions

References

21 Exact, Metaheuristic, and Hybrid Approaches to Multidimensional Knapsack Problems

J. E. Gallardo, C. Cotta, and A. J. Fernández

21.1 Introduction

21.2 Multidimensional Knapsack Problem

21.3 Hybrid Models

21.4 Experimental Analysis

21.5 Conclusions

References

22 Greedy Seeding and Problem-Specific Operators for GAs Solution of Strip Packing Problems

C. Salto, J. M. Molina, and E. Alba

22.1 Introduction

22.2 Background

22.3 Hybrid GA for the 2SPP

22.4 Genetic Operators for Solving the 2SPP

22.5 Initial Seeding

22.6 Implementation of the Algorithms

22.7 Experimental Analysis

22.8 Conclusions

References

23 Solving the KCT Problem: Large-Scale Neighborhood Search and Solution Merging

C. Blum and M. J. Blesa

23.1 Introduction

23.2 Hybrid Algorithms for the KCT Problem

23.3 Experimental Analysis

23.4 Conclusions

References

24 Experimental Study of GA-Based Schedulers in Dynamic Distributed Computing Environments

F. Xhafa and J. Carretero

24.1 Introduction

24.2 Related Work

24.3 Independent Job Scheduling Problem

24.4 Genetic Algorithms for Scheduling in Grid Systems

24.5 Grid Simulator

24.6 Interface for Using a GA-Based Scheduler with the Grid Simulator

24.7 Experimental Analysis

24.8 Conclusions

References

25 Remote Optimization Service

J. García-Nieto, F. Chicano, and E. Alba

25.1 Introduction

25.2 Background and State of the Art

25.3 ROS Architecture

25.4 Information Exchange in ROS

25.5 XML in ROS

25.6 Wrappers

25.7 Evaluation of ROS

25.8 Conclusions

References

26 Remote Services for Advanced Problem Optimization

J. A. Gómez, M. A. Vega, J. M. Sánchez, J. L. Guisado, D. Lombraña, and F. Fernández

26.1 Introduction

26.2 SIRVA

26.3 MOSET and TIDESI

26.4 ABACUS

References

INDEX