The first edition of Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques was originally put together to offer a basic introduction to the various search and optimization techniques that students might need to use during their research, and this new edition continues this tradition. Search Methodologies has been expanded and brought completely up to date, including new chapters covering scatter search, GRASP, and very large neighborhood search. The chapter authors are drawn from across Computer Science and Operations Research and include some of the world's leading authorities in their field. The book provides useful guidelines for implementing the methods and frameworks described and offers valuable tutorials to students and researchers in the field.

"As I embarked on the pleasant journey of reading through the chapters of this book, I became convinced that this is one of the best sources of introductory material on thesearch methodologies topic to be found. The book's subtitle, "Introductory Tutorials in Optimization and Decision Support Techniques", aptly describes its aim, and the editors and contributors to this volume have achieved this aim with remarkable success. The chapters in this book are exemplary in giving useful guidelines for implementing the methods and frameworks described." Fred Glover, Leeds School of Business, University of Colorado Boulder, USA

"[The book] aims to present a series of well written tutorials by the leading experts in their fields. Moreover, it does this by covering practically the whole possible range of topics in the discipline. It enables students and practitioners to study and appreciate the beauty and the power of some of the computational search techniques that are able to effectively navigate through search spaces that are sometimes inconceivably large. I am convinced that this second edition will build on the success of the first edition andthat it will prove to be just as popular." Jacek Blazewicz, Institute of Computing Science, Poznan University of Technology and Institute of Bioorganic Chemistry, Polish Academy of Sciences

Autorentext

Edmund K. Burke is Deputy Principal for Research at the University of Stirling. His research interests lie at the interface of Operational Research and Computer Science. He is a member of the EPSRC Strategic Advisory Team for Mathematics. He is also a Fellow of the Operational Research Society and the British Computer Society and a member of the UK Computing Research Committee (UKCRC). Professor Burke is Editor-in-chief of the Journal of Scheduling, Area Editor (for Combinatorial Optimisation) of the Journal of Heuristics, Associate Editor of the INFORMS Journal on Computing, Associate Editor of the IEEE Transactions on Evolutionary Computation and a member of the Editorial Board of Memetic Computing. He has edited/authored 14 books and published over 230 refereed papers.

Graham Kendall is the Dunford Professor of Computer Science and a member of the Automated Scheduling, Optimisation and Planning Research Group, School of Computer Science, University of Nottingham, Nottingham, U.K. He is the Deputy Head of the group, which has 9 members of academic staff, about 15 Research Associates/Fellows and about 40 PhD students. He was awarded a BSc (Hons) First Class in Computation from the University of Manchester Institute of Science and Technology (UMIST), UK in 1997 and received his PhD from The University of Nottingham (School of Computer Science) in 2000. He is a Fellow of the Operational Research Society. Professor Kendall's expertise lies in Operational Research, Meta- and Hyper-Heuristics, Evolutionary Computation and Artificial Intelligence, with a specific interest in scheduling, including timetabling, sports scheduling, cutting and packing and rostering. He has published over 35 refereed journal papers (the vast majority in ISI ranked journals) and over 90 peer reviewed conference papers. He has edited 12 books and authored 10 book chapters.

Inhalt

Introduction.- Classical Techniques.- Integer Programming.- Genetic Algorithms.- Scatter Search.- Genetic Programming.- Artificial Immune Systems.- Swarm Intelligence.- Tabu Search.- Simulated Annealing.- GRASP: Greedy Randomized Adaptive Search Procedures.- Variable Neighborhood Search.- Very Large-Scale Neighborhood Search.- Constraint Programming.- Multi-objective Optimization.- Sharpened and Focused No Free Lunch and Complexity Theory.- Machine Learning.- Fuzzy Reasoning.- Rough-Set-Based Decision Support.- Hyper-heuristics.- Approximations and Randomization.- Fitness Landscapes.