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Stochastic constraint programming by neuroevolution with filtering

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

5 Alıntılar (Scopus)

Özet

Stochastic Constraint Programming is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several complete solution methods have been proposed, but the authors recently showed that an incomplete approach based on neuroevolution is more scalable. In this paper we hybridise neuroevolution with constraint filtering on hard constraints, and show both theoretically and empirically that the hybrid can learn more complex policies more quickly.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıIntegration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems - 7th International Conference, CPAIOR 2010, Proceedings
Sayfalar282-286
Sayfa sayısı5
DOI'lar
Yayın durumuYayınlandı - 2010
Etkinlik7th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2010 - Bologna, !!Italy
Süre: 14 Haz 201018 Haz 2010

Yayın serisi

AdıLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim6140 LNCS
ISSN (Basılı)0302-9743
ISSN (Elektronik)1611-3349

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???event.eventtypes.event.conference???7th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2010
Ülke/Bölge!!Italy
ŞehirBologna
Periyot14/06/1018/06/10

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