Parallel LAN/WAN heuristics for optimization
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Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
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Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Efficient parallel LAN/WAN algorithms for optimization: the MALLBA project
Parallel Computing
A comparison of PSO and GA approaches for gene selection and classification of microarray data
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Efficient parallel LAN/WAN algorithms for optimization. The mallba project
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PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
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Transactions on Petri nets and other models of concurrency IV
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An XML specification for automatic parallel dynamic programming
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A parallel algorithm for the two-dimensional cutting stock problem
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Journal of Heuristics
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The MALLBA project tackles the resolution of combinatorial optimization problems using algorithmic skeletons implemented in C++. mallba offers three families of generic resolution methods: exact, heuristic and hybrid. Moreover, for each resolution method, MALLBA provides three different implementations: sequential, parallel for local area networks, and parallel for wide area networks (currently under development). This paper explains the architecture of the MALLBA library, presents some of its skeletons, and offers several computational results to show the viability of the approach.