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

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 co...

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Vydáno v:Genetic programming and evolvable machines Ročník 15; číslo 1; s. 3 - 35
Hlavní autoři: Pappa, Gisele L., Ochoa, Gabriela, Hyde, Matthew R., Freitas, Alex A., Woodward, John, Swan, Jerry
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.03.2014
Témata:
Algorithms
Artificial Intelligence
Automation
Biomedical Engineering and Bioengineering
Classification
Communities
Compilers
Computer Science
Electrical Engineering
Evolutionary algorithms
Genetics
Interpreters
Optimization
Programming
Programming Languages
Programming Techniques
Software Engineering/Programming and Operating Systems
Hyper-heuristics
Automated algorithm design
Meta-learning
Genetic programming
ISSN:1389-2576, 1573-7632
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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 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.
AbstractList 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.
Author Hyde, Matthew R.
Swan, Jerry
Pappa, Gisele L.
Ochoa, Gabriela
Woodward, John
Freitas, Alex A.
Author_xml – sequence: 1
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  givenname: Gabriela
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  givenname: John
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  organization: Computing Science and Mathematics, University of Stirling
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  organization: Computing Science and Mathematics, University of Stirling
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Issue 1
Keywords Hyper-heuristics
Automated algorithm design
Meta-learning
Genetic programming
Language English
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PublicationTitle Genetic programming and evolvable machines
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Snippet The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms...
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SubjectTerms Algorithms
Artificial Intelligence
Automation
Biomedical Engineering and Bioengineering
Classification
Communities
Compilers
Computer Science
Electrical Engineering
Evolutionary algorithms
Genetics
Interpreters
Optimization
Programming
Programming Languages
Programming Techniques
Software Engineering/Programming and Operating Systems
Title Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
URI https://link.springer.com/article/10.1007/s10710-013-9186-9
https://www.proquest.com/docview/1520980736
Volume 15
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