國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data-Driven Evolutionary Optimization/ by Yaochu Jin, Handing Wang, Chaoli Sun.
其他題名:	Integrating Evolutionary Computation, Machine Learning and Data Science /
作者:	Jin, Yaochu.
其他作者:	Sun, Chaoli.
面頁冊數:	XXV, 393 p. 159 illus., 76 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial Intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-74640-7
ISBN:	9783030746407

Data-Driven Evolutionary Optimization = Integrating Evolutionary Computation, Machine Learning and Data Science /
Jin, Yaochu.

Data-Driven Evolutionary OptimizationIntegrating Evolutionary Computation, Machine Learning and Data Science /[electronic resource] :by Yaochu Jin, Handing Wang, Chaoli Sun. - 1st ed. 2021. - XXV, 393 p. 159 illus., 76 illus. in color.online resource. - Studies in Computational Intelligence,9751860-9503 ;. - Studies in Computational Intelligence,564.

Introduction to Optimization -- Classical Optimization Algorithms -- Evolutionary and Swarm Optimization -- Introduction to Machine Learning -- Data-Driven Surrogate-Assisted Evolutionary Optimization -- Multi-Surrogate-Assisted Single-Objective Optimization -- Surrogate-Assisted Multi-Objective Evolutionary Optimization.

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.

ISBN: 9783030746407

Standard No.: 10.1007/978-3-030-74640-7doiSubjects--Topical Terms:

646849
Artificial Intelligence.

LC Class. No.: TA345-345.5

Dewey Class. No.: 620.00285

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