SLS 2007, CfP -- Engineering Stochastic Local Search Algorithms
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Engineering Stochastic Local Search Algorithms
Designing, Implementing and Analyzing Effective Heuristics
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
+ 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.
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
Thomas Stuetzle, IRIDIA, CoDE, ULB, Brussels, Belgium
Mauro Birattari, IRIDIA, CoDE, ULB, Brussels, Belgium
Holger H. Hoos, CS Department, UBC, Vancouver, Canada
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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