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Titlebook: Evolutionary Multi-Criterion Optimization; 9th International Co Heike Trautmann,Günter Rudolph,Christian Grimme Conference proceedings 2017

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書(shū)目名稱(chēng)Evolutionary Multi-Criterion Optimization
副標(biāo)題9th International Co
編輯Heike Trautmann,Günter Rudolph,Christian Grimme
視頻videohttp://file.papertrans.cn/318/317987/317987.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Evolutionary Multi-Criterion Optimization; 9th International Co Heike Trautmann,Günter Rudolph,Christian Grimme Conference proceedings 2017
描述This book constitutes the refereed proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Münster, Germany in March 2017.?. The 33 revised full papers presented together with 13 poster presentations were carefully reviewed and selected from 72 submissions. The EMO 2017 aims to discuss all aspects of EMO development and deployment, including theoretical foundations; constraint handling techniques; preference handling techniques; handling of continuous, combinatorial or mixed-integer problems; local search techniques; hybrid approaches; stopping criteria; parallel EMO models; performance evaluation; test functions and benchmark problems; algorithm selection approaches; many-objective optimization; large scale optimization; real-world applications; EMO algorithm implementations..
出版日期Conference proceedings 2017
關(guān)鍵詞big data; evolutionary algorithms; machine learning; numeric computing; parallel computing; algorithm ana
版次1
doihttps://doi.org/10.1007/978-3-319-54157-0
isbn_softcover978-3-319-54156-3
isbn_ebook978-3-319-54157-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation,ctive optimisation algorithm is automatically configured in a multi-objective fashion, and our results demonstrate that this approach can produce very good results as well as interesting insights into the efficacy of various strategies and components of a flexible multi-objective local search framew
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Timing the Decision Support for Real-World Many-Objective Optimization Problems, authors counter this limitation, by integrating a termination criterion with an MOEA run, towards determining the appropriate timing for application of the machine learning based framework. Results based on three real-world many-objective problems considered in this paper, highlight the utility of
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