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Editorial for the Special Issue on Automated Design and Assessment of Heuristic Search Methods

Gabriela Ochoa 1 Mike Preuss 2 Thomas Bartz-Beielstein 3 Marc Schoenauer 4
2 Department of Computer Science
Department of Computer Science
4 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : 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 of providing the final user with automated tools to assist the tuning, design and assessment of heuristic optimisation methods.
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Gabriela Ochoa, Mike Preuss, Thomas Bartz-Beielstein, Marc Schoenauer. Editorial for the Special Issue on Automated Design and Assessment of Heuristic Search Methods. Evolutionary Computation, Massachusetts Institute of Technology Press (MIT Press), 2012, 20 (2), pp.161-163. ⟨hal-01160793⟩

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