Screen LibraryData-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...
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| Main Authors: | |
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| Group Author: | ; |
| Published: |
Springer,
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| Publisher Address: | Cham, Switzerland : |
| Publication Dates: | [2021] |
| Literature type: | Book |
| Language: | English |
| Series: |
Studies in computational intelligence,
volume 975 |
| Subjects: | |
| Summary: |
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. |
| Carrier Form: | xxv, 393 pages : illustrations (some color) ; 24 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9783030746391 3030746399 |
| Index Number: | QA402 |
| CLC: | O224 |
| Call Number: | O224/J617 |
| Contents: | 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. |