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 Название: Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science
Автор: Yaochu Jin, Handing Wang, Chaoli Sun
Издательство: Springer
Год: 2021
Формат: PDF
Страниц: 408
Размер: 13,5 Mb
Язык: English
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.
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Автор: vitvikvas 29-06-2021, 05:28 | Напечатать |
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Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science 
