Titlebook: Learning and Intelligent Optimization; 4th International Co Christian Blum,Roberto Battiti Conference proceedings 2010 Springer-Verlag Berl

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Adaptive “Anytime” Two-Phase Local Searchroblems for the bi-objective case. In particular, we propose two weight setting strategies that show better anytime search characteristics than the original weight setting strategy used in the TPLS algorithm.

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Adaptive “Anytime” Two-Phase Local Searchquence of single-objective ones by means of weighted sum aggregations. This paper studies different sequences of weights for defining the aggregated problems for the bi-objective case. In particular, we propose two weight setting strategies that show better anytime search characteristics than the or

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Adaptive Filter SQPjor innovation in this work. The resulting algorithm can deal with constraints involving different length scales without requiring their normalization. The effort related to gradients computation is compensated by achieving superlinear local convergence rate (under some hypothesis on the problem, th

SLING

Algorithm Selection as a Bandit Problem with Unbounded Lossespensive. In recent work, we adopted an . approach, in which a performance model is iteratively updated and used to guide selection on a sequence of problem instances. The resulting . trade-off was represented as a bandit problem with expert advice, using an existing solver for this game, but this re

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Distance Functions, Clustering Algorithms and Microarray Data Analysisrray data. In the general data mining and classification literature, functions such as Euclidean distance or Pearson correlation have gained their status of . standards thanks to a considerable amount of experimental validation. For microarray data, the issue of which distance function “works best”