© 2007

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

International Workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007. Proceedings

  • Editors
  • Thomas Stützle
  • Mauro Birattari
  • Holger H. Hoos
Conference proceedings SLS 2007

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4638)

Table of contents

  1. Front Matter
  2. The Importance of Being Careful

    1. Arne Løkketangen
      Pages 1-15
    2. Paola Pellegrini, Mauro Birattari
      Pages 31-45
    3. Enda Ridge, Daniel Kudenko
      Pages 46-60
    4. Frank Neumann, Dirk Sudholt, Carsten Witt
      Pages 61-75
    5. Jørgen Bang-Jensen, Marco Chiarandini, Yuri Goegebeur, Bent Jørgensen
      Pages 91-105
    6. Roberto Battiti, Franco Mascia
      Pages 106-120
    7. Mutsunori Yagiura, Akira Komiya, Kenya Kojima, Koji Nonobe, Hiroshi Nagamochi, Toshihide Ibaraki et al.
      Pages 121-135
    8. Hisafumi Kokubugata, Ayako Moriyama, Hironao Kawashima
      Pages 136-149
    9. Christian Blum, Maria J. Blesa
      Pages 150-161
  3. Short Papers

    1. Jaya Sreevalsan-Nair, Meike Verhoeven, David L. Woodruff, Ingrid Hotz, Bernd Hamann
      Pages 182-186
    2. Joseph M. Pasia, Karl F. Doerner, Richard F. Hartl, Marc Reimann
      Pages 187-191
    3. Hideki Hashimoto, Youichi Ezaki, Mutsunori Yagiura, Koji Nonobe, Toshihide Ibaraki, Arne Løkketangen
      Pages 192-196
    4. Thijs Urlings, Rubén Ruiz
      Pages 202-206
    5. Takashi Imamichi, Hiroshi Nagamochi
      Pages 207-211

About these proceedings


Stochastic local search (SLS) algorithms enjoy great popularity as powerful and versatile tools for tackling computationally hard decision and optimization pr- lems from many areas of computer science, operations research, and engineering. To a large degree, this popularity is based on the conceptual simplicity of many SLS methods and on their excellent performance on a wide gamut of problems, ranging from rather abstract problems of high academic interest to the very s- ci?c problems encountered in many real-world applications. SLS methods range from quite simple construction procedures and iterative improvement algorithms to more complex general-purpose schemes, also widely known as metaheuristics, 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, and overall resembled more an art than a science. However, in recent years it has become evident that at the core of this development task there is a highly complex engineering process, which combines various aspects of algorithm design with empirical analysis techniques and problem-speci?c background, and which relies heavily on knowledge from a number of disciplines and areas, including computer science, operations research, arti?cial intelligence, and statistics. This development process needs to be - sisted by a sound methodology that addresses the issues arising in the various phases of algorithm design, implementation, tuning, and experimental eval- tion.


AI/OR techniques Boolean function algorithm perfornamce algorithms behavior of SLS algorithms calculus dynamic behavior linear optimization metaheuristic methodological developments optimization programming search space analysis tuning procedures visualization

Bibliographic information

Industry Sectors
IT & Software