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Table of contents

  1. Front Matter
    Pages i-xxi
  2. Search Strategies

    1. Front Matter
      Pages 1-1
    2. Marc Sevaux, Kenneth Sörensen, Nelishia Pillay
      Pages 3-21
    3. José Fernando Gonçalves, Mauricio G. C. Resende
      Pages 23-37
    4. Simone de Lima Martins, Isabel Rosseti, Alexandre Plastino
      Pages 39-87
    5. Michael Emmerich, Ofer M. Shir, Hao Wang
      Pages 89-119
    6. Martina Fischetti, Matteo Fischetti
      Pages 121-153
    7. Rafael Martí, Jose A. Lozano, Alexander Mendiburu, Leticia Hernando
      Pages 155-175
    8. Carlos A. Coello Coello
      Pages 177-204
    9. Oleg V. Shylo, Oleg A. Prokopyev
      Pages 205-220
  3. Local Search

    1. Front Matter
      Pages 221-221
    2. Laurent Michel, Pascal Van Hentenryck
      Pages 223-260
    3. Abdullah Alsheddy, Christos Voudouris, Edward P. K. Tsang, Ahmad Alhindi
      Pages 261-297
    4. W. Michiels, E. H. L. Aarts, J. Korst
      Pages 299-339
    5. Abraham Duarte, Jesús Sánchez-Oro, Nenad Mladenović, Raca Todosijević
      Pages 341-367
  4. Metaheuristics

    1. Front Matter
      Pages 369-369
    2. Manuel López-Ibáñez, Thomas Stützle, Marco Dorigo
      Pages 371-407
    3. David Corne, Michael A. Lones
      Pages 409-430
    4. Carlos García-Martínez, Francisco J. Rodriguez, Manuel Lozano
      Pages 431-464
    5. Paola Festa, Mauricio G. C. Resende
      Pages 465-488
    6. Michael G. Epitropakis, Edmund K. Burke
      Pages 489-545
    7. Thomas Stützle, Rubén Ruiz
      Pages 547-577
    8. Thomas Stützle, Rubén Ruiz
      Pages 579-605
    9. Carlos Cotta, Luke Mathieson, Pablo Moscato
      Pages 607-638
    10. Konstantinos E. Parsopoulos
      Pages 639-685
    11. Éric D. Taillard, Stefan Voß
      Pages 687-701
    12. José Fernando Gonçalves, Mauricio G. C. Resende
      Pages 703-715
    13. Rafael Martí, Ángel Corberán, Juanjo Peiró
      Pages 717-740
    14. Manuel Laguna
      Pages 741-758
    15. Pierre Hansen, Nenad Mladenović
      Pages 759-787
  5. Analysis and Implementation

    1. Front Matter
      Pages 789-789
    2. Kenneth Sörensen, Marc Sevaux, Fred Glover
      Pages 791-808
    3. Teodor Gabriel Crainic
      Pages 809-847
    4. Per Kristian Lehre, Pietro S. Oliveto
      Pages 849-884
  6. Applications

    1. Front Matter
      Pages 885-885
    2. Jaume Barceló, Hanna Grzybowska, Jesús Arturo Orozco
      Pages 887-930
    3. Ramón Alvarez-Valdes, Maria Antónia Carravilla, José Fernando Oliveira
      Pages 931-977
    4. Fernando Sandoya, Anna Martínez-Gavara, Ricardo Aceves, Abraham Duarte, Rafael Martí
      Pages 979-998
    5. Sune S. Nielsen, Grégoire Danoy, Wiktor Jurkowski, Roland Krause, Reinhard Schneider, El-Ghazali Talbi et al.
      Pages 999-1023
    6. Eduardo G. Pardo, Rafael Martí, Abraham Duarte
      Pages 1025-1049
    7. Christopher Expósito-Izquierdo, Eduardo Lalla-Ruiz, Jesica de Armas, Belén Melián-Batista, J. Marcos Moreno-Vega
      Pages 1051-1077
    8. Andrea Valsecchi, Enrique Bermejo, Sergio Damas, Oscar Cordón
      Pages 1079-1101
    9. Roger Z. Ríos-Mercado
      Pages 1103-1121
    10. Luciana S. Buriol
      Pages 1123-1140
    11. Vinicius Morais, Fernanda S. H. Souza, Geraldo R. Mateus
      Pages 1141-1161
    12. Yannis Marinakis, Magdalene Marinaki, Athanasios Migdalas
      Pages 1163-1196
    13. Rubén Ruiz
      Pages 1197-1220
    14. Christian Blum, Paola Festa
      Pages 1221-1240
    15. Helena Ramalhinho Lourenço, Martín Gómez Ravetti
      Pages 1241-1258
    16. Oleksandra Yezerska, Sergiy Butenko
      Pages 1259-1289

About this book

Introduction

Heuristics are strategies using readily accessible, loosely applicable information to control problem solving. Algorithms, for example, are a type of heuristic. By contrast, Metaheuristics are methods used to design Heuristics and may coordinate the usage of several Heuristics toward the formulation of a single method. GRASP (Greedy Randomized Adaptive Search Procedures) is an example of a Metaheuristic. To the layman, heuristics may be thought of as ‘rules of thumb’ but despite its imprecision, heuristics is a very rich field that refers to experience-based techniques for problem-solving, learning, and discovery. Any given solution/heuristic is not guaranteed to be optimal but heuristic methodologies are used to speed up the process of finding satisfactory solutions where optimal solutions are impractical. The introduction to this Handbook provides an overview of the history of Heuristics along with main issues regarding the methodologies covered. This is followed by Chapters containing various examples of local searches, search strategies and Metaheuristics, leading to an analyses of Heuristics and search algorithms. The reference concludes with numerous illustrations of the highly applicable nature and implementation of Heuristics in our daily life. Each chapter of this work includes an abstract/introduction with a short description of the methodology. Key words are also necessary as part of top-matter to each chapter to enable maximum search engine optimization. Next, chapters will include discussion of the adaptation of this methodology to solve a difficult optimization problem, and experiments on a set of representative problems.

Keywords

Algorithms, Heuristics Metaheuristics Search Strategies local search

Editors and affiliations

  • Rafael Martí
    • 1
  • Panos M.  Pardalos
    • 2
  • Mauricio G. C. Resende
    • 3
  1. 1.Statistics and Operations Research DepartmentUniversity of ValenciaValenciaSpain
  2. 2.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Amazon.com, Inc. and University of WashingtonSeattleUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-07124-4
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-07123-7
  • Online ISBN 978-3-319-07124-4
  • Buy this book on publisher's site