PUBLICATION DATE

2012-05-28

TITLE

Machine learning for global optimization

ISSUE

4

VOLUME

63

ISSN (print)

0020-580X

ISSN (electronic)

1476-9352

ABSTRACT

In this paper we introduce the LeGO (Learning for Global Optimization) approach for global optimization in which machine learning is used to predict the outcome of a computationally expensive global optimization run, based upon a suitable training performed by standard runs of the same global optimization method. We propose to use a Support Vector Machine (although different machine learning tools might be employed) to learn the relationship between the starting point of an algorithm and the final outcome (which is usually related to the function value at the point returned by the procedure). Numerical experiments performed both on classical test functions and on difficult space trajectory planning problems show that the proposed approach can be very effective in identifying good starting points for global optimization.

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27 TRIPLES      23 PREDICATES      27 URIs      15 LITERALS

Subject Predicate Object
1 articles:ad9373cf3130547ee13764acddaf8c2d sg:abstract In this paper we introduce the LeGO (Learning for Global Optimization) approach for global optimization in which machine learning is used to predict the outcome of a computationally expensive global optimization run, based upon a suitable training performed by standard runs of the same global optimization method. We propose to use a Support Vector Machine (although different machine learning tools might be employed) to learn the relationship between the starting point of an algorithm and the final outcome (which is usually related to the function value at the point returned by the procedure). Numerical experiments performed both on classical test functions and on difficult space trajectory planning problems show that the proposed approach can be very effective in identifying good starting points for global optimization.
2 sg:ddsIdJournalBrand iaor
3 sg:doi 10.1057/iaor.2012.35771
4 sg:doiLink http://dx.doi.org/10.1057/iaor.2012.35771
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12 sg:hasJournalBrand journal-brands:11eaa1206191d0347361452c8e00709c
13 sg:issnElectronic 1476-9352
14 sg:issnPrint 0020-580X
15 sg:issue 4
16 sg:license http://scigraph.springernature.com/explorer/license/
17 sg:npgId iaor2012221
18 sg:pageEnd
19 sg:pageStart
20 sg:publicationDate 2012-05-28
21 sg:publicationYear 2012
22 sg:publicationYearMonth 2012-05
23 sg:scigraphId ad9373cf3130547ee13764acddaf8c2d
24 sg:title Machine learning for global optimization
25 sg:volume 63
26 rdf:type sg:Article
27 rdfs:label Article: Machine learning for global optimization
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