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Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms (2013)
| Venue: | GENET PROGRAM EVOLVABLE MACH |
| Citations: | 4 - 1 self |
BibTeX
@MISC{Pappa13contrastingmeta-learning,
author = {Gisele L. Pappa and G. Ochoa and M. R. Hyde and A. A. Freitas and J. Woodward and J. Swan},
title = { Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms},
year = {2013}
}
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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
Keyphrases
evolutionary algorithm hyper-heuristic research genet program evolvable mach doi important role algorithm space supervised machine learning automated algorithm design problem space recent work important research direction common goal different level several survey hyper-heuristics method historical perspective field share genetic programming hyper-heuristic optimisation foundational issue algorithm design process performance measure important issue machine meta-learning main contribution
