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Metaheuristics, such as simulated annealing, genetic and evolutionary algorithms, tabu search, ant colony optimization, scatter search and iterated local search, 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.

For many years, the main focus of research was on the application of single metaheuristics to given problems. 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.

A quite new field of research is also the hybridization of metaheuristics with other techniques. Recently, it was observed that the incorporation of more classical artificial intelligence and operations research techniques in metaheuristics can be very beneficial. A representative example is the use of constraint programming in order to efficiently explore large neighborhoods.

The design and implementation of hybrid metaheuristics rises problems going beyond questions about the design of a single metaheuristic. Choice and tuning of parameters is for example enlarged by the problem of how to achieve a proper interaction of different algorithm components. Interaction can take place at low-level, using functions from different metaheuristics, but also at high-level, e.g., using a portfolio of metaheuristics for automated hybridization.

It is implicit with the subject of the workshop that contributions should address the combination and comparison of different metaheuristic components and concepts. In contrast to standard research in metaheuristics, also negative results - e.g., a component shows poor performance for the majority of test instances - are of considerable importance in hybridization. Such results have often been ignored, at least in the publication of results in standard metaheuristics research. Further, the above mentioned enlarged selection of parameters will attract more attention to this part of designing algorithms.

In summary, with this workshop we aim at papers that give good examples for carefully designed and well-analyzed hybrid metaheuristics. The extraction of guidelines for the general design of hybrid metaheuristics would be desirable.

The scope of this works includes, 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,
  • theoretic aspects of hybridization,
  • parallelization,
  • software libraries.
Researchers are invited to submit papers of not more than 12 pages. Authors are encourage to submit their papers in LaTeX. Papers must be submitted in LNCS style (see Information for LNCS Authors).

Every paper will be reviewed by at least two members of the program committee. Researchers are explicitly encouraged to address statistical validity of their results, if they compare different approaches. Source code and problem instances should (if relevant) be made available on the Internet.

The submitted papers must be original works. Moreover, simultaneous submission to other conferences with published proceedings is not permitted.

Accepted papers will be published in the Lecture Notes in Computer Science series of Springer-Verlag.

The program committee of HM 2006 will decide on the acceptance of a paper for publication according to a purely scientific basis. HM 2006 shall be a non-profit workshop. The workshop fee will be kept as low as possible.