Heuristic search algorithms have been successfully applied to solve many problems in practice. Their design, however, has increased in complexity as the number of parameters and choices for operators and algorithmic components is also expanding. There is clearly the need for providing the final user with automated tools to assist the tuning, design and assessment of heuristic optimisation methods. In recent years a growing number workshops and tracks has been held to address these issues. In 2010, the Parallel Problem Solving from Nature (PPSN) conference hosted two workshops, which decided to joint efforts to organise this journal special issue. The workshop “Self-Tuning, Self-Configuring and Self-Generating Search Heuristics,” distinguished three general processes in automated heuristic design: 1) tuning: the process of adjusting the algorithm's control parameters, 2) configuring: the process of selecting and using existing algorithmic components such as search operators, construction heuristics or acceptance criteria, and 3) generating: the process...
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Summer 2012
June 01 2012
Editorial for the Special Issue on Automated Design and Assessment of Heuristic Search Methods
In Special Collection:
CogNet
Gabriela Ochoa,
Gabriela Ochoa
School of Computer Science, University of Nottingham, Nottingham, UK [email protected]
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Mike Preuss,
Mike Preuss
Department of Computer Science, TU Dortmund, Dortmund, Germany [email protected]
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Thomas Bartz-Beielstein,
Thomas Bartz-Beielstein
Department of Computer Science, Fachhochschule Köln, Gummersbach, Germany [email protected]
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Marc Schoenauer
Marc Schoenauer
INRIA Saclay - Île-de-France, France [email protected]
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Gabriela Ochoa
School of Computer Science, University of Nottingham, Nottingham, UK [email protected]
Mike Preuss
Department of Computer Science, TU Dortmund, Dortmund, Germany [email protected]
Thomas Bartz-Beielstein
Department of Computer Science, Fachhochschule Köln, Gummersbach, Germany [email protected]
Marc Schoenauer
INRIA Saclay - Île-de-France, France [email protected]
Online ISSN: 1530-9304
Print ISSN: 1063-6560
© 2012 by the Massachusetts Institute of Technology
2012
Evolutionary Computation (2012) 20 (2): 161–163.
Citation
Gabriela Ochoa, Mike Preuss, Thomas Bartz-Beielstein, Marc Schoenauer; Editorial for the Special Issue on Automated Design and Assessment of Heuristic Search Methods. Evol Comput 2012; 20 (2): 161–163. doi: https://doi.org/10.1162/EVCO_e_00071
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