Learning and Intelligent Optimization

Volume 7997 of the series Lecture Notes in Computer Science pp 404-409


Batched Mode Hyper-heuristics

  • Shahriar AstaAffiliated withSchool of Computer Science, University of Nottingham
  • , Ender ÖzcanAffiliated withSchool of Computer Science, University of Nottingham
  • , Andrew J. ParkesAffiliated withSchool of Computer Science, University of Nottingham Email author 

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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.


Combinatorial optimisation Metaheuristics Hyper-heuristics