Natural Computing Series
cover

Automated Design of Machine Learning and Search Algorithms

Editors: Pillay, Nelishia, Qu, Rong (Eds.)

  • Presents recent advances across automated machine learning and automated algorithm design
  • Contains a useful introduction to the fast-developing area of automated design of machine learning
  • Includes contributions by leading researchers from multiple disciplines
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  • ISBN 978-3-030-72069-8
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About this book

This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection.

The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field.

The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.

About the authors

Nelishia Pillay is a professor at the University of Pretoria in South Africa. She holds the Multichoice Joint-Chair in Machine Learning and SARChI Chair in Artificial Intelligence. She is chair of the IEEE Technical Committee on Intelligent Systems Applications, IEEE Task Force on Hyper-Heuristics and the IEEE Task Force on Automated Algorithm Design, Configuration and Selection. Her research areas include hyper-heuristics, automated design of machine learning and search techniques, combinatorial optimization, genetic programming, genetic algorithms and deep learning. These are the focus areas of the NICOG (Nature-Inspired Computing Optimization) research group which she has established.

Rong Qu is an associate professor at the School of Computer Science, University of Nottingham. Her research interests include the modeling and optimisation of combinatorial optimisation problems in optimisation research and artificial intelligence. These include evolutionary algorithms, mathematical programming and metaheuristics integrated with machine learning to automate the design of intelligent algorithms. Dr. Qu is an associated editor at IEEE Computational Intelligence Magazine, IEEE Transactions on Evolutionary Computation, Journal of Operational Research Society and PeerJ Computer Science. She is a Senior IEEE Member since 2012 and the Vice-Chair of Evolutionary Computation Task Committee and Technical Committee on Intelligent Systems Applications at IEEE Computational Intelligence Society.

Buy this book

eBook n/a
  • ISBN 978-3-030-72069-8
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover n/a
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Bibliographic Information

Bibliographic Information
Book Title
Automated Design of Machine Learning and Search Algorithms
Editors
  • Nelishia Pillay
  • Rong Qu
Series Title
Natural Computing Series
Copyright
2021
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-72069-8
DOI
10.1007/978-3-030-72069-8
Hardcover ISBN
978-3-030-72068-1
Series ISSN
1619-7127
Edition Number
1
Number of Pages
X, 190
Number of Illustrations
35 b/w illustrations
Topics