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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Veröffentlicht in:	Genetic programming and evolvable machines Jg. 15; H. 1; S. 3 - 35
Hauptverfasser:	Pappa, Gisele L., Ochoa, Gabriela, Hyde, Matthew R., Freitas, Alex A., Woodward, John, Swan, Jerry
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.03.2014
Schlagworte:	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 givenname: Gisele L. surname: Pappa fullname: Pappa, Gisele L. email: glpappa@dcc.ufmg.br organization: Computer Science Department, Universidade Federal de Minas Gerais – sequence: 2 givenname: Gabriela surname: Ochoa fullname: Ochoa, Gabriela organization: Computing Science and Mathematics, University of Stirling – sequence: 3 givenname: Matthew R. surname: Hyde fullname: Hyde, Matthew R. organization: School of Environmental Sciences, University of East Anglia – sequence: 4 givenname: Alex A. surname: Freitas fullname: Freitas, Alex A. organization: School of Computing, University of Kent – sequence: 5 givenname: John surname: Woodward fullname: Woodward, John organization: Computing Science and Mathematics, University of Stirling – sequence: 6 givenname: Jerry surname: Swan fullname: Swan, Jerry organization: Computing Science and Mathematics, University of Stirling
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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
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