Handbook of Metaheuristics

Volume 146 of the series International Series in Operations Research & Management Science pp 449-468


A Classification of Hyper-heuristic Approaches

  • Edmund K. BurkeAffiliated withAutomated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science, University of Nottingham Email author 
  • , Matthew HydeAffiliated withThe University of Nottingham
  • , Graham KendallAffiliated withThe University of Nottingham
  • , Gabriela OchoaAffiliated withThe University of Nottingham
  • , Ender ÖzcanAffiliated withThe University of Nottingham
  • , John R. WoodwardAffiliated withThe University of Nottingham

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The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present an overview of previous categorisations of hyper-heuristics and provide a unified classification and definition, which capture the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goals are to clarify the mainfeatures of existing techniques and to suggest new directions for hyper-heuristic research.