Table of contents

  1. Kenneth Sörensen, Marc Sevaux, Fred Glover
  2. Marc Sevaux, Kenneth Sörensen, Nelishia Pillay
  3. Manuel López-Ibáñez, Thomas Stützle, Marco Dorigo
  4. José Fernando Gonçalves, Mauricio G. C. Resende
  5. Jaume Barceló, Hanna Grzybowska, Jesús Arturo Orozco
  6. Laurent Michel, Pascal Van Hentenryck
  7. Ramón Alvarez-Valdes, Maria Antónia Carravilla, José Fernando Oliveira
  8. Simone de Lima Martins, Isabel Rosseti, Alexandre Plastino
  9. Fernando Sandoya, Anna Martı́nez-Gavara, Ricardo Aceves, Abraham Duarte, Rafael Martı́
  10. Michael Emmerich, Ofer M. Shir, Hao Wang
  11. Sune S. Nielsen, Grégoire Danoy, Wiktor Jurkowski, Roland Krause, Reinhard Schneider, El-Ghazali Talbi et al.
  12. Carlos García-Martínez, Francisco J. Rodriguez, Manuel Lozano
  13. Paola Festa, Mauricio G. C. Resende
  14. Abdullah Alsheddy, Christos Voudouris, Edward P. K. Tsang, Ahmad Alhindi
  15. Michael G. Epitropakis, Edmund K. Burke
  16. Thomas Stützle, Rubén Ruiz
  17. Thomas Stützle, Rubén Ruiz
  18. Eduardo G. Pardo, Rafael Martí, Abraham Duarte
  19. Christopher Expósito-Izquierdo, Eduardo Lalla-Ruiz, Jesica de Armas, Belén Melián-Batista, J. Marcos Moreno-Vega
  20. Martina Fischetti, Matteo Fischetti

About this book


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.

Editors and affiliations

  • Rafael Martí
    • 1
  • Pardalos Panos
    • 2
  • Mauricio G. C. Resende
    • 3
  1. 1.Department of Statistics and OperationsUniversity of ValenciaValenciaSpain
  2. 2.University of Florida Industrial and Systems EngineeringGainesvilleUSA
  3. 3.Modeling and Optimization GroupAmazon (United States) SeattleUSA

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Online ISBN 978-3-319-07153-4