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 ebook 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 conscientiously reviewed and chosen from a complete of ninety nine submissions. The papers specialise in the intersections and uncharted territories among laptop studying, man made intelligence, mathematical programming and algorithms for tough optimization difficulties. as well as the paper contributions the convention additionally incorporated three invited audio system, who awarded leading edge examine effects and frontiers, and three instructional talks, which have been an important in bringing jointly the several parts of LION community.

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14 E. Burns and W. Ruml Although IDA* tended to have reasonable CPU time performance in this domain, its growth factors were very close to one. The only reason that IDA* achieved reasonable performance is because expansions in this synthetic tree domain required virtually no computation. This would not be observed in a more realistic domain where expansion required any reasonable computation. 3 Summary When trained off-line, the incremental model was able to make predictions on the 15-puzzle domain that were nearly indistinguishable from CDP, the current state-of-the art.

C Springer-Verlag Berlin Heidelberg 2012 32 M. Caserta and S. Voß production cost and the unitary inventory holding cost for item j in period t, respectively. Parameters mjt and ajt indicate the setup time and the unitary production time, respectively, while bt stands for the production capacity in period t. Parameter djt indicates the demand of item j in period t. , yjt ∈ {0, 1}, which takes value 1 if there is a setup for item j in period t; as well as xjt ≥ 0 and sjt ≥ 0, indicating the production volume and the inventory level for item j in period t, respectively.

However, this is not the only reason because some tests were achieved always using all the transformations available in LD (by artificially setting the probability to select any of them to 1) and the best results were only found 269 times. Indeed, one of the forces of LD and ALS are that unlike VND which wastes some time trying to unsuccessfully optimize with a transformation before switching to the next one, LD uses any interesting transformation and concentrates on the most performing ones. Overall, VNS-LD performs better than VNS-ALS even if this last algorithm’s training time is not considered here (training was achieved before the tests on smaller graphs); furthermore, VNS-LD’s training was erased between two tests and the system had to learn from scratch at each run.

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