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INTRODUCTION
Metaheuristics have received considerable interest in the fields of applied artificial intelligence and combinatorial optimization. Plenty of hard problems in a huge variety of areas, including bioinformatics, logistics, engineering, business, etc., have been tackled successfully with metaheuristic approaches. For many problems the resulting algorithms are considered to be the state-of-the-art methods.
In recent years, it has become evident that the concentration on a sole metaheuristic is rather restrictive. A skilled combination of concepts of different metaheuristics, a so called hybrid metaheuristic, can provide a more efficient behavior and a higher flexibility when dealing with real-world and large-scale problems. Also the hybridization of metaheuristics with other AI/OR techniques, such as integer linear programming and constraint programming, has been proven to be very effective.
SCOPE AND TOPICS OF INTEREST
The HM 2006 workshop aims at papers that give good examples for carefully designed and well-analyzed hybrid metaheuristics.
Topics of interest include, but is not limited to:
- novel combinations of components from different metaheuristics,
- hybridization of metaheuristics and AI/OR techniques,
- low-level hybridization,
- high-level hybridization, portfolio techniques, expert systems,
- co-operative search,
- taxonomy, terminology, classification of hybrid metaheuristics,
- co-evolution techniques,
- automated parameter tuning,
- empirical and statistical comparison,
- theoretical aspects of hybridization,
- parallelization,
- software libraries.