A Classification of Hyper-heuristics Approaches – Revisited

Hyper-heuristics comprise a set of approaches that aim to automate the development of computational search methodologies, initially to address operational research problems but more recently venturing into new domains such as bioinformatics, strategies for games, and software engineering. This chapter overviews previous categorisations of hyper-heuristics and provides a unified classification and definition. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail, and recent research trends are highlighted. Our goals are to clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research. Edmund K. Burke Queen Mary University of London, UK. e-mail: ekb@qmul.ac.uk Graham Kendall University of Nottingham Malaysia Campus, Malaysia. Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science, University of Nottingham, UK. Matthew Hyde at Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science, University of Nottingham, UK. Gabriela Ochoa Computing Science and Mathematics, University of Stirling, UK. Ender Özcan Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science, University of Nottingham, UK. John R. Woodward Queen Mary University of London, UK.

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