CALL FOR PAPERS
Call for Papers is also available as a flyer
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
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.
TOPICS OF INTEREST
The scope of this works includes, but is not limited,
- 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,
- 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.
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.