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Titlebook: Natural Computing for Simulation-Based Optimization and Beyond; Silja Meyer-Nieberg,Nadiia Leopold,Tobias Uhlig Book 2020 The Author(s), u

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發(fā)表于 2025-3-21 16:22:42 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Natural Computing for Simulation-Based Optimization and Beyond
編輯Silja Meyer-Nieberg,Nadiia Leopold,Tobias Uhlig
視頻videohttp://file.papertrans.cn/662/661695/661695.mp4
概述Bridges the gap between the areas of simulation studies and optimization with natural computing.First consideration of evolutionary data farming and digital games in simulation-based optimization.Exam
叢書名稱SpringerBriefs in Operations Research
圖書封面Titlebook: Natural Computing for Simulation-Based Optimization and Beyond;  Silja Meyer-Nieberg,Nadiia Leopold,Tobias Uhlig Book 2020 The Author(s), u
描述This SpringerBrief bridges the gap between the areas of simulation studies on the one hand, and optimization with natural computing on the other. Since natural computing methods have been applied with great success in several application areas, a review concerning potential benefits and pitfalls for simulation studies is merited. The brief presents such an overview and combines it with an introduction to natural computing and selected major approaches, as well as with a concise treatment of general simulation-based optimization. As such, it is the first review which covers both the methodological background and recent application cases.?.The brief is intended to serve two purposes: First, it can be used to gain more information concerning natural computing, its major dialects, and their usage for simulation studies. It also covers the areas of multi-objective optimization and neuroevolution. While the latter is only seldom mentioned in connection withsimulation studies, it is a powerful potential technique. Second, the reader is provided with an overview of several areas of simulation-based optimization which range from logistic problems to engineering tasks. Additionally, the brie
出版日期Book 2020
關(guān)鍵詞Simulation-Based Optimization; Simulation; Natural Computing; Evolutionary Algorithms; Swarm-Based Metho
版次1
doihttps://doi.org/10.1007/978-3-030-26215-0
isbn_softcover978-3-030-26214-3
isbn_ebook978-3-030-26215-0Series ISSN 2195-0482 Series E-ISSN 2195-0504
issn_series 2195-0482
copyrightThe Author(s), under exclusive license to Springer Nature Switzerland AG 2020
The information of publication is updating

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Simulation-Based Optimization,ards, approaches are presented in which a simulation model is directly coupled with an optimizer based on natural computing. These examples mostly concern the case of parameter optimization. As pointed out in several parts of the paper, conducting simulations is usually time-consuming. Therefore, ef
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Conclusions,e and Darwin or swarming behavior observed in bird flocks or insect swarms. Going back to the 1960s when the first approaches originated, today the area has emerged as a wide and mature research field with many application areas.
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urtain had divided the world into two camps, a fact heavily underlined by the Berlin crises at the end of the forties and the Korean war at the beginning of the fifties. The tensions of the Cold War dominated political decisions everywhere. To England the new distribution of international power mean
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Silja Meyer-Nieberg,Nadiia Leopold,Tobias Uhlige.g. Agatha Christie and Michael Innes) has established itself as a legitimate form of literary entertainment, and this is also, I believe, the case with the good secret-agent story (e.g. le Carré and Len Deighton) and the psychological thriller (e.g. Patricia Highsmith and Ira Levin). But there see
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