@inbook{4fd4fa4097b64a8bb7ffa1a2d2843b89,
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 = "Steven Prestwich and Tarim, {S. Armagan} and R. Rossi and Brahim Hnich",
year = "2010",
month = jun,
day = "14",
doi = "10.1007/978-3-642-13520-0_30",
language = "English",
isbn = "978-3-642-13519-4",
volume = "6140 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Berlin / Heidelberg",
pages = "282--286",
booktitle = "Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems",
}