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

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

This e-book constitutes the completely refereed post-conference court cases 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 conscientiously reviewed and chosen from a complete of ninety nine submissions. The papers specialize in the intersections and uncharted territories among desktop studying, synthetic intelligence, mathematical programming and algorithms for challenging optimization difficulties. as well as the paper contributions the convention additionally integrated three invited audio system, who provided vanguard study effects and frontiers, and three educational talks, that have been the most important in bringing jointly different parts of LION community.

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

Example text

Depending on the current graph, changing the value of the diameter by 1 may involve a large number of transformations and no strategy is powerful enough in this case. This phenomena is called plateau and some practical ways to handle it are described in [9,1]. 4 Conclusion From these experiments, we first notice that the VNS-LD and VNS-ALS algorithms are clearly more efficient even if the performance of VNS-VND e is overestimated. The good performance of the VNS-LD may be due to the wide range of transformations implicitly considered in VNS-LD that are not implemented in VNS-VND.

The proposed approach belongs to the class of math-heuristic algorithms, since we use mathematical programming techniques in a heuristic fashion [9].

Thus, if we eliminate such hard constraints, we obtain a problem that can easily be decomposed into smaller (easy) subproblems, one per item. Let us indicate with: ⎧ ⎫ sjt−1 + xjt = djt + sjt ⎪ ⎪ ⎪ ⎪ ⎨ ⎬ xjt ≤ M yjt sub Xj = yjt ∈ {0, 1} ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ xjt , sjt ≥ 0 the set of feasible solutions for the j th subproblem. It is easy to observe that Xjsub defines the feasible region for the single item uncapacitated lot sizing problem. k , xkjt , skjt , with k = 1, . . , Kj , Let us now indicate with xkj , where xkj = yjt sub the set of extreme points of conv(Xj ).

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