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Informationen zum Autor 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.?? Klappentext Real-world problems and modern optimization techniques to solve themHere, 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. 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. Inhaltsverzeichnis 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. Leguizam ón, G. Ord ó ñez, S. Molina, and E. Alba 6.1 Introduction 83 6.2 Dynamic Optimization Problems 84 6.3 Canonical...
