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Anotace knihy

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.

Parametry knihy

Zařazení knihy Knihy v angličtině Computing & information technology Databases

• Plný název: Data-Driven Evolutionary Optimization
• Podnázev: Integrating Evolutionary Computation, Machine Learning and Data Science
• Autor: Yaochu Jin, Handing Wang, Chaoli Sun
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 393
• EAN: 9783030746421
• ID: 39282744
• Nakladatelství: Springer, Berlin
• Hmotnost: 584 g
• Rozměry: 235 × 155 × 23 mm
• Rok vydání: 2022

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