Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics

Second International Workshop, SLS 2009, Brussels, Belgium, September 3-4, 2009. Proceedings

  • Thomas Stützle
  • Mauro Birattari
  • Holger H. Hoos
Conference proceedings SLS 2009

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5752)

Table of contents

  1. Front Matter
  2. High-Performance Local Search for Task Scheduling with Human Resource Allocation

    1. Bertrand Estellon, Frédéric Gardi, Karim Nouioua
      Pages 1-15
    2. Celso C. Ribeiro, Isabel Rosseti, Reinaldo Vallejos
      Pages 16-30
    3. Andrew M. Sutton, Adele E. Howe, L. Darrell Whitley
      Pages 31-45
    4. Andrew M. Sutton, Adele E. Howe, L. Darrell Whitley
      Pages 46-60
    5. Claudio F. Lima, Martin Pelikan, Fernando G. Lobo, David E. Goldberg
      Pages 61-75
    6. Christian Horoba, Dirk Sudholt
      Pages 76-91
  3. Short Papers

    1. Thierry Benoist, Bertrand Estellon, Frédéric Gardi, Antoine Jeanjean
      Pages 105-109
    2. Pieter Vansteenwegen, Wouter Souffriau, Dirk Van Oudheusden
      Pages 110-114
    3. Daniela Favaretto, Elena Moretti, Paola Pellegrini
      Pages 115-119
    4. Arnaud Liefooghe, Salma Mesmoudi, Jérémie Humeau, Laetitia Jourdan, El-Ghazali Talbi
      Pages 120-124
    5. Gregory Gutin, Daniel Karapetyan
      Pages 125-129
    6. Julien Robet, Frédéric Lardeux, Frédéric Saubion
      Pages 130-134
    7. Stefano Benedettini, Andrea Roli, Luca Di Gaspero
      Pages 135-139
    8. David C. Matthews, Andrew M. Sutton, Doug Hains, L. Darrell Whitley
      Pages 145-149
    9. Matteo Gagliolo, Catherine Legrand, Mauro Birattari
      Pages 150-154
  4. Back Matter

About these proceedings


Stochastic local search (SLS) algorithms are established tools for the solution of computationally hard problems arising in computer science, business adm- istration, engineering, biology, and various other disciplines. To a large extent, their success is due to their conceptual simplicity, broad applicability and high performance for many important problems studied in academia and enco- tered in real-world applications. SLS methods include a wide spectrum of te- niques, ranging from constructive search procedures and iterative improvement algorithms to more complex SLS methods, such as ant colony optimization, evolutionary computation, iterated local search, memetic algorithms, simulated annealing, tabu search, and variable neighborhood search. Historically, the development of e?ective SLS algorithms has been guided to a large extent by experience and intuition. In recent years, it has become - creasingly evident that success with SLS algorithms depends not merely on the adoption and e?cient implementation of the most appropriate SLS technique for a given problem, but also on the mastery of a more complex algorithm - gineering process. Challenges in SLS algorithm development arise partly from the complexity of the problems being tackled and in part from the many - grees of freedom researchers and practitioners encounter when developing SLS algorithms. Crucial aspects in the SLS algorithm development comprise al- rithm design, empirical analysis techniques, problem-speci?c background, and background knowledge in several key disciplines and areas, including computer science, operations research, arti?cial intelligence, and statistics.


Scheduling algorithm performance algorithms ant colony autonomous systems combinatorial optimization complexity genetic algorithms k-sat metaheuristic optimization search algorithms search space shortest path visualization

Editors and affiliations

  • Thomas Stützle
    • 1
  • Mauro Birattari
    • 2
  • Holger H. Hoos
    • 3
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium
  2. 2.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium
  3. 3.Computer Science DepartmentUniversity of British ColumbiaVancouverCanada

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-642-03750-4
  • Online ISBN 978-3-642-03751-1
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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