Kangaroo: An Efficient Constraint-Based Local Search System using Lazy Propagation

In this paper, we introduce Kangaroo, a constraint-based local search system. While existing systems such as Comet maintain invariants after every move, Kangaroo adopts a lazy strategy, updating invariants only when they are needed. Our empirical evaluation shows that Kangaroo consistently has a smaller memory footprint than Comet, and is usually significantly faster.In this paper, we introduce Kangaroo, a constraint-based local search system. While existing systems such as Comet maintain invariants after every move, Kangaroo adopts a lazy strategy, updating invariants only when they are needed. Our empirical evaluation shows that Kangaroo consistently has a smaller memory footprint than Comet, and is usually significantly faster.
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Conference Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

6876 LNCS

Publisher URI

http://www.dmi.unipg.it/cp2011/papers.html

DOI

https://doi.org/10.1007/978-3-642-23786-7_49

Copyright Statement

© 2011 Springer Berlin / Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com

Subject

Artificial Intelligence and Image Processing not elsewhere classified

Publication URI

http://hdl.handle.net/10072/40879

Collection

Conference outputs