A Classification of Hyper-heuristics Approaches

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InCollection{Burke:2010:HBMH,

• author = "Edmund K. Burke and Matthew Hyde and Graham Kendall and Gabriela Ochoa and Ender Ozcan and John R. Woodward",
• title = "A Classification of Hyper-heuristics Approaches",
• booktitle = "Handbook of Metaheuristics",
• publisher = "Springer",
• year = "2010",
• editor = "Michel Gendreau and Jean-Yves Potvin",
• volume = "57",
• series = "International Series in Operations Research \& Management Science",
• chapter = "15",
• pages = "449--468",
• edition = "2nd",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-4419-1663-1",
• URL = "http://www.cs.nott.ac.uk/~gxo/papers/ChapterClassHH.pdf",
• DOI = "doi:10.1007/978-1-4419-1665-5_15",
• abstract = "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 and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures 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 goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research.",

}

Genetic Programming entries for Edmund Burke Matthew R Hyde Graham Kendall Gabriela Ochoa Ender Ozcan John R Woodward

Citations