International Conference on Learning and Intelligent Optimization

LION 2013: Learning and Intelligent Optimization pp 404-409

Batched Mode Hyper-heuristics

Conference paper

DOI: 10.1007/978-3-642-44973-4_43

Volume 7997 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Asta S., Özcan E., Parkes A. (2013) Batched Mode Hyper-heuristics. In: Nicosia G., Pardalos P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science, vol 7997. Springer, Berlin, Heidelberg

Abstract

A primary role for hyper-heuristics is to control search processes based on moves generated by neighbourhood operators. Studies have shown that such hyper-heuristics can be effectively used, without modification, for solving unseen problem instances not only from a particular domain, but also on different problem domains. They hence provide a general-purpose software component to help reduce the implementation time needed for effective search methods. However, hyper-heuristic studies have generally used time-contract algorithms (i.e. a fixed execution time) and also solved each problem instance independently. We consider the potential gains and challenges of a hyper-heuristic being able to treat a set of instances as a batch; to be completed within an overall joint execution time. In batched mode, the hyper-heuristic can freely divide the computational effort between the individual instances, and also exploit what it learns on one instance to help solve other instances.

Keywords

Combinatorial optimisationMetaheuristicsHyper-heuristics

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK