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Titlebook: Evolutionary Computation in Combinatorial Optimization; 9th European Confere Carlos Cotta,Peter Cowling Conference proceedings 2009 Springe

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書目名稱Evolutionary Computation in Combinatorial Optimization
副標(biāo)題9th European Confere
編輯Carlos Cotta,Peter Cowling
視頻videohttp://file.papertrans.cn/318/317875/317875.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Evolutionary Computation in Combinatorial Optimization; 9th European Confere Carlos Cotta,Peter Cowling Conference proceedings 2009 Springe
描述This book constitutes the refereed proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2009, held in Tübingen, Germany, in April 2009. The 21 revised full papers presented were carefully reviewed and selected from 53 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms and ant colony optimization.
出版日期Conference proceedings 2009
關(guān)鍵詞Variable; algorithm; algorithms; combinatorial optimization; evolutionary algorithm; greedy randomized ad
版次1
doihttps://doi.org/10.1007/978-3-642-01009-5
isbn_softcover978-3-642-01008-8
isbn_ebook978-3-642-01009-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/317875.jpg
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https://doi.org/10.1007/978-3-642-01009-5Variable; algorithm; algorithms; combinatorial optimization; evolutionary algorithm; greedy randomized ad
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Exact Solutions to the Traveling Salesperson Problem by a Population-Based Evolutionary Algorithm,upper bounds on the runtime until an optimum solution has been found. To the best of our knowledge this is the first time it has been shown that an .-hard problem is solved exactly instead of approximated only by a black box algorithm.
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