A Classification of Hyper-heuristic Approaches

Citation

Burke E, Hyde M, Kendall G, Ochoa G, Ozcan E & Woodward J (2009) A Classification of Hyper-heuristic Approaches. Computer Science Technical Report, NOTTCS-TR-SUB-0906241359-0664. University of Nottingham. http://www.cs.stir.ac.uk/~jrw/publications/AClassificationHyperHeuristicApproaches.pdf

Abstract
Hyper-heuristics comprise a set of approaches that share the common goal of automating the design and tuning of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. The term hyper-heuristic was coined in the early 2000's to refer to the idea of ‘heuristics to choose heuristics'. However, the idea of automating the heuristic design process can be traced back to the early 1960's. With the incorporation of Genetic Programming into hyper-heuristic research, a new type of hyper-heuristics has emerged that we have termed ‘heuristics to generate heuristics'. In this paper we overview previous categorisations of hyper-heuristics and propose a unified classification. Our goal is to both clarify the main features of existing approaches and to suggest new directions for hyper-heuristic research.

People

Professor Gabriela Ochoa

Professor, Computing Science