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Record Nr. |
UNINA9910483596403321 |
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Titolo |
Recent Advances in Computational Optimization : Results of the Workshop on “Computational Optimization” and “Numerical Search and Optimization” 2018 / / edited by Stefka Fidanova |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (X, 237 p. 114 illus., 65 illus. in color.) |
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Collana |
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Studies in Computational Intelligence, , 1860-949X ; ; 838 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Algorithms |
Computational Intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Preface -- Organization -- Chapter 1. Developing a Method for Measuring the Failover Times of First Hop Redundancy within Video Networks -- Chapter 2. Desktop application developed by open source tools for optimizations in cases of natural hazards and field response. Chapter 3. Data optimizations on Kresna Fire (2017) as inputs for WFA simulations -- Chpater 4. Solving Sorting of Rolling Stock Problems utilizing pseudochain structures in Graphs, etc. |
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Sommario/riassunto |
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The book presents a comprehensive collection of extended contributions from the Workshops on Computational Optimization and Numerical Search and Optimization 2018. Our everyday life is unthinkable without optimization as we try to minimize our effort while maximizing profit. Many real-world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. The book discusses recent advances in computational optimization to address important real-world problems like modeling of physical processes, wildfire, natural hazards and metal nanostructures, workforce planning, wireless network |
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topology, parameter settings for controlling different processes, extracting elements from video clips, and management of cloud computing environment. It shows readers how to develop algorithms for these problems based on new intelligent methods like evolutionary computations, ant colony optimization, and constraint programming, and demonstrates how some real-world problems arising in engineering, economics and other domains can be formulated as optimization problems. |
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