Scatter Search

Methodology and Implementations in C

  • Manuel Laguna
  • Rafael Martí

Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 24)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Manuel Laguna, Rafael Martí
    Pages 1-21
  3. Manuel Laguna, Rafael Martí
    Pages 23-47
  4. Manuel Laguna, Rafael Martí
    Pages 49-68
  5. Manuel Laguna, Rafael Martí
    Pages 69-87
  6. Manuel Laguna, Rafael Martí
    Pages 89-122
  7. Manuel Laguna, Rafael Martí
    Pages 123-139
  8. Manuel Laguna, Rafael Martí
    Pages 141-183
  9. Manuel Laguna, Rafael Martí
    Pages 185-218
  10. Manuel Laguna, Rafael Martí
    Pages 219-254
  11. Manuel Laguna, Rafael Martí
    Pages 255-276
  12. Back Matter
    Pages 277-291

About this book


The book Scatter Search by Manuel Laguna and Rafael Martí represents a long-awaited "missing link" in the literature of evolutionary methods. Scatter Search (SS)-together with its generalized form called Path Relinking-constitutes the only evolutionary approach that embraces a collection of principles from Tabu Search (TS), an approach popularly regarded to be divorced from evolutionary procedures. The TS perspective, which is responsible for introducing adaptive memory strategies into the metaheuristic literature (at purposeful level beyond simple inheritance mechanisms), may at first seem to be at odds with population-based approaches. Yet this perspective equips SS with a remarkably effective foundation for solving a wide range of practical problems. The successes documented by Scatter Search come not so much from the adoption of adaptive memory in the range of ways proposed in Tabu Search (except where, as often happens, SS is advantageously coupled with TS), but from the use of strategic ideas initially proposed for exploiting adaptive memory, which blend harmoniously with the structure of Scatter Search. From a historical perspective, the dedicated use of heuristic strategies both to guide the process of combining solutions and to enhance the quality of offspring has been heralded as a key innovation in evolutionary methods, giving rise to what are sometimes called "hybrid" (or "memetic") evolutionary procedures. The underlying processes have been introduced into the mainstream of evolutionary methods (such as genetic algorithms, for example) by a series of gradual steps beginning in the late 1980s.


algorithms code evolution genetic algorithms knowledge linear optimization memory nonlinear optimization optimization problem solving programming scheduling

Editors and affiliations

  • Manuel Laguna
    • 1
  • Rafael Martí
    • 2
  1. 1.University of ColoradoUSA
  2. 2.University of ValenciaSpain

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media New York 2003
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4020-7376-2
  • Online ISBN 978-1-4615-0337-8
  • Series Print ISSN 1387-666X
  • Buy this book on publisher's site