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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Authors: Pappa, Gisele L
Ochoa, Gabriela
Hyde, Matthew
Freitas, Alex A
Woodward, John
Swan, Jerry
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Keywords: Hyper-heuristics
Genetic programming
Automated algorithm design
Issue Date: Mar-2014
Publisher: Springer
Citation: Pappa GL, Ochoa G, Hyde M, Freitas AA, Woodward J & Swan J (2014) Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms, Genetic Programming and Evolvable Machines, 15 (1), pp. 3-35.
Abstract: The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to promote awareness and cross-fertilisation of ideas across the (by and large, non-overlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.
Type: Journal Article
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Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Federal University of Minas Gerais
Computing Science - CSM Dept
University of East Anglia
University of Kent
Computing Science and Mathematics
Computing Science - CSM Dept

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