Enumerative approaches to combinatorial optimization - part I
Annals of Operations Research
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PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
MALLBA: A Library of Skeletons for Combinatorial Optimisation (Research Note)
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
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IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
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ICPADS '01 Proceedings of the Eighth International Conference on Parallel and Distributed Systems
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HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The mallba project tackles the resolution of combinatorial optimization problems using generic algorithmic skeletons implemented in C++. A skeleton in the mallba library implements an optimization method in one of the three families of generic optimization techniques offered: exact, heuristic and hybrid. Moreover, for each of those methods, mallba provides three different implementations: sequential, parallel for Local Area Networks, and parallel for Wide Area Networks. This paper introduces the architecture of the mallba library, details some of the implemented skeletons, and offers computational results for some classical optimization problems to show the viability of our library. Among other conclusions, we claim that the design used to develop the optimization techniques included in the library is generic and efficient at the same time.