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- Author: Yaochu Jin
- Publisher: Springer
- ISBN-10: 3030746429
- ISBN-13: 9783030746421
- Format: 15.6 x 23.4 x 2.2 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
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.
EXTRA 10 % discount with code: EXTRA
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The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Yaochu Jin
- Publisher: Springer
- ISBN-10: 3030746429
- ISBN-13: 9783030746421
- Format: 15.6 x 23.4 x 2.2 cm, minkšti viršeliai
- Language: English 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.
Reviews