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Download Learning and Intelligent Optimization: 6th International by Ethan Burns, Wheeler Ruml (auth.), Youssef Hamadi, Marc PDF

By Ethan Burns, Wheeler Ruml (auth.), Youssef Hamadi, Marc Schoenauer (eds.)

This booklet constitutes the completely refereed post-conference lawsuits of the sixth overseas convention on studying and clever Optimization, LION 6, held in Paris, France, in January 2012. The 23 lengthy and 30 brief revised papers have been rigorously reviewed and chosen from a complete of ninety nine submissions. The papers specialise in the intersections and uncharted territories among computer studying, synthetic intelligence, mathematical programming and algorithms for challenging optimization difficulties. as well as the paper contributions the convention additionally incorporated three invited audio system, who provided vanguard examine effects and frontiers, and three educational talks, that have been an important in bringing jointly different parts of LION community.

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Extra info for Learning and Intelligent Optimization: 6th International Conference, LION 6, Paris, France, January 16-20, 2012, Revised Selected Papers

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Variable neighborhood search for extremal graphs. 1. the autographix system. Discrete Math. 212, 29–44 (2000) 10. : Variable neighborhood search for extremal graphs. v. three ways to automate finding conjectures. Discrete Math. 276, 81–94 (2004) 11. : Variable neighborhood search for extremal graphs. iii. on the largest eigenvalue of color-constrained trees. Linear and Multilinear Algebra 49, 143–160 (2001) 12. : Variable neighborhood search for extremal graph. 7. polyenes with maximum homo-lumo gap.

Domain-independent construction of pattern database heuristics for cost-optimal planning. In: Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI 2007) (July 2007) 3. : Iterative-deepening-A*: An optimal admissible tree search. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 1985), pp. 1034–1036 (1985) 4. : Time complexity of iterative-deepening-A*. Artificial Intelligence 129, 199–218 (2001) 5. : Frontier search. Journal of the ACM 52(5), 715–748 (2005) 6.

Item j ∈ [1, n]. Let us also assume we indicate with Y = y 1 , . . , y k , . . , y Kj the set of all feasible setup plans for item j. Let us now consider a setup schedule yk = y1k , . . , yTk ∈ Y and let us define the set of induced setup schedules as: Y(k) = (y1 , . . , yT ) : yt ≤ ytk , yt ∈ {0, 1} , t = 1, . . , assuming that yk defines s setups, the set Y(k) contains 2s setup schedules. Obviously, for each setup schedule in Y(k) there exists a unique dominant Wagner-Whitin production plan.

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