Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Pappa:2013:GPEM,
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author = "Gisele L. Pappa and Gabriela Ochoa and
Matthew R. Hyde and Alex A. Freitas and John Woodward and Jerry Swan",
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title = "Contrasting meta-learning and hyper-heuristic
research: the role of evolutionary algorithms",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2014",
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volume = "15",
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number = "1",
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pages = "3--35",
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month = mar,
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keywords = "genetic algorithms, genetic programming,
Hyper-heuristics, Meta-learning, Automated algorithm
design",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-013-9186-9",
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size = "33 pages",
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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 emphasise
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.",
- }
Genetic Programming entries for
Gisele L Pappa
Gabriela Ochoa
Matthew R Hyde
Alex Alves Freitas
John R Woodward
Jerry Swan
Citations