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SLS 2007, CfP -- Engineering Stochastic Local Search Algorithms

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  • Thomas St├╝tzle
    *** Apologies if you receive this CFP more than once *** Engineering Stochastic Local Search Algorithms ... Designing, Implementing and Analyzing Effective
    Message 1 of 1 , Nov 28, 2006
      *** Apologies if you receive this CFP more than once ***

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

      SLS 2007

      6-8 September, 2007. Brussels, Belgium

      More details and up-to-date information at

      Scope of the Workshop

      Stochastic local search (SLS) algorithms are among the most powerful
      techniques for solving computationally hard problems in many areas
      of computer science, operations research and engineering. SLS
      techniques range from rather simple constructive and iterative
      improvement algorithms to general-purpose methods, also widely known
      as metaheuristics, such as ant colony optimization, evolutionary
      computation, iterated local search, memetic algorithms, simulated
      annealing, tabu search and variable neighbourhood search.

      In recent years, it has become evident that the development of
      effective SLS algorithms is a highly complex engineering process
      that typically combines aspects of algorithm design and
      implementation with empirical analysis and problem-specific
      background knowledge. The difficulty of this process is due in part
      to the complexity of the problems being tackled, and in part to the
      large number of degrees of freedom researchers and practitioners
      face when developing SLS algorithms.

      This development process needs to be assisted by a sound methodology
      that adresses the issues arising in the phases of algorithm design,
      implementation, tuning and experimental evaluation. In addition,
      more research is required to understand which SLS techniques are
      best suited for particular problem types and to better understand
      the relationship between algorithm components, parameter settings,
      problem characteristics and performance.

      Relevant Research Areas

      The aim of this workshop is to stress the importance of an
      integration of relevant aspects of SLS research into a more coherent
      engineering methodology and to bring together researchers that work
      in various fields, including computer science, operations research,
      metaheuristics, algorithmics, statistics and application areas.

      SLS 2007 solicits contributions dealing with any aspect of
      engineering stochastic local search algorithms. Typical, but not
      exclusive, topics of interest are:

      + Methodological developments for the implementation of SLS
      algorithms (engineering procedures, integration of tools in the
      SLS engineering process, ...)

      + In-depth experimental studies of SLS algorithms (behavior of SLS
      algorithms, comparison of SLS algorithms, ...), problem
      characteristics (search space analysis, ...) and their impact on
      algorithm performance.

      + Tools for the assistance in the development process of SLS
      algorithms (software libraries, automatic and semi-automatic
      tuning procedures, learning techniques, ...).

      + Case studies in the principled development of well designed SLS
      algorithms for practically relevant problems.

      + Aspects that become relevant when moving from "classical"
      NP-hard problems to those including multiple objectives,
      stochastic information or dynamically changing data.

      + New algorithmic developments (usage of AI/OR techniques, large
      scale neighbourhood searches, new SLS methods, data structures,
      distributed algorithms, ...)

      + Theoretical analysis of SLS behaviour and their impact on
      algorithm design (analysis of operators, dynamic behaviour of
      SLS algorithms, ...)


      The workshop proceedings will be published in Springer's Lecture
      Notes in Computer Science (LNCS) series.

      Important Dates

      Submission deadline 16 March, 2007
      Notification of acceptance 21 May, 2007
      Camera ready copy 4 June, 2007
      Workshop 6-8 September, 2007

      SLS 2007 Workshop Committee

      General Chairs

      Thomas Stuetzle, IRIDIA, CoDE, ULB, Brussels, Belgium
      Mauro Birattari, IRIDIA, CoDE, ULB, Brussels, Belgium
      Holger H. Hoos, CS Department, UBC, Vancouver, Canada

      Further Information

      Up-to-date information will be published on the web site
      www.stochastic-local-search.net/sls07. For information about local
      arrangements, registration forms, etc., please refer to the
      above-mentioned web site or contact the local organizers.

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