@inbook{ab3f0694358344bf99a87966d10ac32c,
title = "Stochastic constraint programming by neuroevolution with filtering",
abstract = "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.",
author = "Prestwich, \{Steve D.\} and Tarim, \{S. Armagan\} and Roberto Rossi and Brahim Hnich",
year = "2010",
doi = "10.1007/978-3-642-13520-0\_30",
language = "English",
isbn = "3642135196",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "282--286",
booktitle = "Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems - 7th International Conference, CPAIOR 2010, Proceedings",
note = "7th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2010 ; Conference date: 14-06-2010 Through 18-06-2010",
}