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portada Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science
Type
Physical Book
Author
Publisher
Language
English
Pages
393
Format
Paperback
Dimensions
23.4x15.6x2.2 cm
Weight
0.59 kg.
ISBN13
9783030746421
Categories

Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science

Yaochu Jin (Author) · Handing Wang (Author) · Chaoli Sun (Author) · Springer · Paperback

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

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Synopsis "Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science "

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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