Discovering beneficial cooperative structures for the automatic construction of heuristics
The current research trends on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristi...
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| Format: | Book Section |
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Springer-Verlag
2010
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| Online Access: | https://eprints.nottingham.ac.uk/35594/ |
| _version_ | 1848795116646432768 |
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| author | Terrazas, German Landa-Silva, Dario Krasnogor, Natalio |
| author_facet | Terrazas, German Landa-Silva, Dario Krasnogor, Natalio |
| author_sort | Terrazas, German |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The current research trends on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for specific problems, that is, the input to the algorithm are problems and the output are problem-tailored heuristics. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problemspecific and effective strategy. Thus, hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem in hand. Some approaches like genetic programming have been proposed for this. In this paper, we report on an alternative methodology that sheds light on simple methodologies that efficiently cooperate by means of local interactions. These entities are seen as building blocks, the combination of which is employed for the automated manufacture of good performing heuristic search strategies.We present proof-of-concept results of applying this methodology to instances of the well-known symmetric TSP. The goal here is to demonstrate feasibility rather than compete with state of the art TSP solvers. This TSP is chosen only because it is an easy to state and well known problem. |
| first_indexed | 2025-11-14T19:26:58Z |
| format | Book Section |
| id | nottingham-35594 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:26:58Z |
| publishDate | 2010 |
| publisher | Springer-Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-355942020-05-04T20:25:05Z https://eprints.nottingham.ac.uk/35594/ Discovering beneficial cooperative structures for the automatic construction of heuristics Terrazas, German Landa-Silva, Dario Krasnogor, Natalio The current research trends on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for specific problems, that is, the input to the algorithm are problems and the output are problem-tailored heuristics. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problemspecific and effective strategy. Thus, hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem in hand. Some approaches like genetic programming have been proposed for this. In this paper, we report on an alternative methodology that sheds light on simple methodologies that efficiently cooperate by means of local interactions. These entities are seen as building blocks, the combination of which is employed for the automated manufacture of good performing heuristic search strategies.We present proof-of-concept results of applying this methodology to instances of the well-known symmetric TSP. The goal here is to demonstrate feasibility rather than compete with state of the art TSP solvers. This TSP is chosen only because it is an easy to state and well known problem. Springer-Verlag 2010-05 Book Section PeerReviewed Terrazas, German, Landa-Silva, Dario and Krasnogor, Natalio (2010) Discovering beneficial cooperative structures for the automatic construction of heuristics. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Studies in Computational Intelligence (284). Springer-Verlag, Berlin, pp. 89-100. ISBN 9783642125386 hyperheuristics cooperative heuristics heuristics metaheuristics http://link.springer.com/chapter/10.1007%2F978-3-642-12538-6_8 doi:10.1007/978-3-642-12538-6_8 doi:10.1007/978-3-642-12538-6_8 |
| spellingShingle | hyperheuristics cooperative heuristics heuristics metaheuristics Terrazas, German Landa-Silva, Dario Krasnogor, Natalio Discovering beneficial cooperative structures for the automatic construction of heuristics |
| title | Discovering beneficial cooperative structures for the automatic construction of heuristics |
| title_full | Discovering beneficial cooperative structures for the automatic construction of heuristics |
| title_fullStr | Discovering beneficial cooperative structures for the automatic construction of heuristics |
| title_full_unstemmed | Discovering beneficial cooperative structures for the automatic construction of heuristics |
| title_short | Discovering beneficial cooperative structures for the automatic construction of heuristics |
| title_sort | discovering beneficial cooperative structures for the automatic construction of heuristics |
| topic | hyperheuristics cooperative heuristics heuristics metaheuristics |
| url | https://eprints.nottingham.ac.uk/35594/ https://eprints.nottingham.ac.uk/35594/ https://eprints.nottingham.ac.uk/35594/ |