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Data-Driven Evolutionary Optimization

Integrating Evolutionary Computation, Machine Learning and Data Science

By: Yaochu Jin,Handing Wang,Chaoli Sun

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Data-Driven Evolutionary Optimization

Integrating Evolutionary Computation, Machine Learning and Data Science

By: Yaochu Jin,Handing Wang,Chaoli Sun

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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Page Count

393

Publisher

Springer Nature

Published Date

ISBN 10

3030746402

ISBN 13

9783030746407

Data-Driven Evolutionary Optimization

Integrating Evolutionary Computation, Machine Learning and Data Science

By: Yaochu Jin,Handing Wang,Chaoli Sun

Write a review Read reviews Take a Quiz Solve Book Puzzle

Data-Driven Evolutionary Optimization

Integrating Evolutionary Computation, Machine Learning and Data Science

By: Yaochu Jin,Handing Wang,Chaoli Sun

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.

Average ratings

N/A

5

0%

4

0%

3

0%

2

0%

1

0%

Solve jigsaw puzzle

Age

Page Count

393

Publisher

Springer Nature

Published Date

ISBN 10

3030746402

ISBN 13

9783030746407