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Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Freitas, Alex A. (2014) Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms. Genetic Programming and Evolvable Machines, 15 (1). pp. 3-35. ISSN 1389-2576. E-ISSN 1573-7632. (doi:10.1007/s10710-013-9186-9) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/s10710-013-9186-9

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.

Item Type: Article
DOI/Identification number: 10.1007/s10710-013-9186-9
Uncontrolled keywords: data mining, machine learning, classification, hyper-heuristic, evolutionary algorithms, optimization
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 15 Oct 2014 17:03 UTC
Last Modified: 29 May 2019 13:12 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/43431 (The current URI for this page, for reference purposes)
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