Optimization Techniques for Solving Complex Problems

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

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. Estebanez 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. Galvan, 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. Saez 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. Leguizamon, G. Ordonez, 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. Fernandez 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. Gomez, M. D. Jaraiz, M. A. Vega, and J. M. Sanchez 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. Sanchez, and J. A. Gomez 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. Sanchez, M. Rubio, M. A. Vega, and J. A. Gomez 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. Leon, G. Miranda, and C. Rodriguez 11.1 Introduction 179 11.2 Algorithm of the Skeleton 180 11.3 Experimental Analysis 185 11.4 Conclusions 189 References 190 12 Tools for Tree Searches: Branch-and-Bound and A Algorithms 193 C. Leon, G. Miranda, and C. Rodriguez 12.1 Introduction 193 12.2 Background 195 12.3 Algorithmic Skeleton for Tree Searches 196 12.4 Experimentation Methodology 199 12.5 Experimental Results 202 12.6 Conclusions 205 References 206 13 Tools for Tree Searches: Dynamic Programming 209 C. Leon, G. Miranda, and C. Rodriguez 13.1 Introduction 209 13.2 Top-Down Approach 210 13.3 Bottom-Up Approach 212 13.4 Automata Theory and Dynamic Programming 215 13.5 Parallel Algorithms 223 13.6 Dynamic Programming Heuristics 225 13.7 Conclusions 228 References 229 Part II Applications 231 14 Automatic Search of Behavior Strategies in Auctions 233 D. Quintana and A. Mochon 14.1 Introduction 233 14.2 Evolutionary Techniques in Auctions 234 14.3 Theoretical Framework: The Ausubel Auction 238 14.4