Información General

• Autores: Edmund K. Burke, Graham Kendall
• Editores: New York : Springer, 2005
• Año de publicación: 2005
• País: Estados Unidos
• Idioma: inglés
• ISBN: 978-0-387-23460-1, 978-0-387-28356-2
• Texto completo no disponible (Saber más ...)

Resumen

• 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.

Otros catálogos

• Red de Bibliotecas Universitarias (REBIUN)

Índice

• 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