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Fast permutation learning

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Author(s)
Wauters, Tony; U0069609; ; JFA; CORA; ;
Verbeeck, Katja; U0060666; ; ; ; ;
De Causmaecker, Patrick; U0003471; ; ; ; ;
Vanden Berghe, Greet; U0053376; ; ; ; JLA;
Contributor(s)
Hamadi, Y; ; ; ; ; ;
Schoenauer, M; ; ; ; ; ;
Keywords
Learning automata
Permutations
Reinforcement Learning
Optimization
ITEC
iMinds

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URI
http://hdl.handle.net/20.500.12424/784301
Online Access
https://lirias.kuleuven.be/handle/123456789/330182
Abstract
Permutations occur in a great variety of optimization problems, such as routing, scheduling and assignment problems. The present paper introduces the use of learning automata for the online learning of good quality permutations. Several centralized and decentralized methods using individual and common rewards are presented. The performance, memory requirement and scalability of the presented methods is analyzed. Results on well known benchmark problems show interesting properties. It is also demonstrated how these techniques are successfully applied to multi-project scheduling problems.
status: published
Date
2012-01-16
Type
Description (Metadata) only
Identifier
oai:lirias.kuleuven.be:123456789/330182
https://lirias.kuleuven.be/handle/123456789/330182
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