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Record Nr. |
UNINA9910488717903321 |
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Autore |
Jin Yaochu |
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Titolo |
Data-Driven Evolutionary Optimization : Integrating Evolutionary Computation, Machine Learning and Data Science / / by Yaochu Jin, Handing Wang, Chaoli Sun |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (408 pages) |
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Collana |
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Studies in Computational Intelligence, , 1860-9503 ; ; 975 |
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Disciplina |
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Soggetti |
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Engineering - Data processing |
Computational intelligence |
Artificial intelligence |
Data Engineering |
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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Introduction to Optimization -- Classical Optimization Algorithms -- Evolutionary and Swarm Optimization -- Introduction to Machine Learning -- Data-Driven Surrogate-Assisted Evolutionary Optimization -- Multi-Surrogate-Assisted Single-Objective Optimization -- Surrogate-Assisted Multi-Objective Evolutionary Optimization. |
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Sommario/riassunto |
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Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization |
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algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included. |
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