Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques

ISBN-13: 9781441936288 / Angielski / Miękka / 2010 / 620 str.

Edmund K. Burke; Graham Kendall

Opis

Search Methodologies is a tutorial survey of the methodologies that are at the confluence of several fields: Computer Science, Mathematics and Operations Research. It is a carefully structured and integrated treatment of the major technologies in optimization and search methodology. The book is made up of 18 chapters. The chapter authors are drawn from across Computer Science and Operations Research and include some of the world s leading authorities in their field. Topical chapters in the book are highlighted in the contents. The result is a major state-of-the-art tutorial text of the main optimization and search methodologies available to researchers, students and practitioners across discipline domains in applied science. It can be used as a textbook or a reference book to learn and apply these methodologies to a wide range of today s problems. It has been written by some of the world s most well known authors in the field."

Recenzje

From the reviews:

"This edited book is ... designed to provide introductions to topics in search methodology. ... The chapters are well-written, accurate and carefully presented and provide a good exposition of each of the topics. ... Although aimed at PhD students, and I would certainly recommend the book to them and to supervisors, this volume should prove very useful to a wider audience ... . It provides an excellent introduction to a large set of techniques in search methodology and is a pleasure to read." (JM Wilson, Journal of the Operational Research Society, Vol. 58 (3), 2007)

Spis treści

Foreword; Fred Glover Preface Chapter 1: Introduction; Edmund Burke and Graham Kendall Chapter 2: Classical Techniques; Kathryn Dowsland Chapter 3: Integer Programming; Bob Bosch and Michael Trick Chapter 4: Genetic Algorithms; Kumara Sastry, David Goldberg, and Graham Kendall Chapter 5: Genetic Programming; John Koza and Riccardo Poli Chapter 6: Tabu Search; Michael Gendreau and Jean-Yves Potvin Chapter 7: Simulated Annealing; Emile Aarts, Jan Korst and Wil Michiels Chapter 8: Variable Neighborhood Search; Pierre Hansen and Nenad Mladenovic Chapter 9: Constraint Programming; Eugene Freuder and Mark Wallace Chapter 10: Multi-Objective Optimization; Kalyanmoy Deb Chapter 11: Complexity Theory and The No Free Lunch Theorem; Darrell Whitley and Jean Paul Watson Chapter 12:Machine Learning; Xin Yao and Yong Liu Chapter 13: Artificial Immune Systems; Uwe Aickelin and Dipankar Dasgupta Chapter 14: Swarm Intelligence; Daniel Merkle and Martin Middendorf Chapter 15: Fuzzy Reasoning; Costas Pappis and Constantinos Siettos Chapter 16: Rough Set Based Decision Support; Roman Slowinski, Salvatore Greco and Benedetto Matarazzo Chapter 17: Hyper-heuristics; Peter Ross Chapter 18:Approximation Algorithms; Carla Gomes and Ryan Williams Chapter 19: Fitness Landscapes; Colin Reeves