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Titlebook: Learning and Intelligent Optimization; 5th International Co Carlos A. Coello Coello Conference proceedings 2011 Springer-Verlag GmbH Berlin

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書目名稱Learning and Intelligent Optimization
副標題5th International Co
編輯Carlos A. Coello Coello
視頻videohttp://file.papertrans.cn/583/582900/582900.mp4
概述Fast-track conference proceedings.State-of-the-art research.Up-to-date results
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Learning and Intelligent Optimization; 5th International Co Carlos A. Coello Coello Conference proceedings 2011 Springer-Verlag GmbH Berlin
描述This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Learning and Intelligent Optimization, LION 5, held in Rome, Italy, in January 2011.The 32 revised regular and 3 revised short papers were carefully reviewed and selected from a total of 99 submissions. In addition to the contributions to the general track there are 11 full papers and 3 short papers presented at the following four special sessions; IMON: Intelligent Multiobjective OptimizatioN, LION-PP: Performance Prediction Self* EAs: Self-tuning, self-configuring and self-generating evolutionary algorithms LION-SWAP: Software and Applications.
出版日期Conference proceedings 2011
關(guān)鍵詞adaptive genetic algorithms; evolutionary computation; meta-heuristics; multi-objective optimization; ve
版次1
doihttps://doi.org/10.1007/978-3-642-25566-3
isbn_softcover978-3-642-25565-6
isbn_ebook978-3-642-25566-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag GmbH Berlin Heidelberg 2011
The information of publication is updating

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Solving the Two-Dimensional Bin Packing Problem with a Probabilistic Multi-start Heuristicard and has a multitude of real world applications. We consider the case where the items are oriented and guillotine cutting is free. In this paper we first present a review of well-know heuristics for the 2BP and then propose a new ILP model for the problem. Moreover, we develop a multi-start algor
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An Optimal Stopping Strategy for Online Calibration in Local Searchof stochastic problems the events of interest belong to two categories (success or failure) and the objective consists in predicting when the last success will take place. The application to a local search setting is immediate if we identify the success with the detection of a new local optimum. Bei
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Instance-Based Parameter Tuning via Search Trajectory Similarity Clusteringthe literature that returns a ”one-size-fits-all” parameter configuration for all instances. This is unsatisfactory since different instances may require the algorithm to use very different parameter configurations in order to find good solutions. There have been approaches that perform instance-bas
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