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Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science - Handing Wang,Chaoli Sun,Yaochu Jin

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2021-06-29
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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- ... Visas aprašymas

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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 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.

Daugiau informacijos

Autorius Handing Wang, Chaoli Sun, Yaochu Jin
Leidėjas Springer Nature Switzerland
Series Studies in Computational Intelligence
Išleidimo metai 2021
Viršelio tipas Kieti viršeliai
EAN 9783030746391
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215,97 € 287,96 €