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Data-driven evolutionary optimizatio...

Jin, Yaochu.

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Data-driven evolutionary optimization = integrating evolutionary computation, machine learning and data science /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven evolutionary optimization/ by Yaochu Jin, Handing Wang, Chaoli Sun.
Reminder of title:	integrating evolutionary computation, machine learning and data science /
Author:	Jin, Yaochu.
other author:	Wang, Handing.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xxv, 393 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Mathematical optimization. -
Online resource:	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. - Cham :Springer International Publishing :2021. - xxv, 393 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.9751860-949X ;. - Studies in computational intelligence ;v.975..

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:

517763
Mathematical optimization.

LC Class. No.: QA402.5 / .J569 2021

Dewey Class. No.: 519.6

Data-driven evolutionary optimization = integrating evolutionary computation, machine learning and data science /
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505 0 $a 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.
520 $a 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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