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Automated design of machine learning...

Pillay, Nelishia.

Automated design of machine learning and search algorithms

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automated design of machine learning and search algorithmsedited by Nelishia Pillay, Rong Qu.
其他作者:	Pillay, Nelishia.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xviii, 187 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-030-72069-8
ISBN:	9783030720698$q(electronic bk.)

Automated design of machine learning and search algorithms
Automated design of machine learning and search algorithms[electronic resource] /edited by Nelishia Pillay, Rong Qu. - Cham :Springer International Publishing :2021. - xviii, 187 p. :ill. (some col.), digital ;24 cm. - Natural computing series,1619-7127. - Natural computing series..

Chapter 1: Recent Developments of Automated Machine Learning and Search Techniques -- Chapter 2: Automated Machine Learning -- Chapter 3: A General Model for Automated Algorithm Design -- Chapter 4: Rigorous Performance Analysis of Hyper-Heuristics -- Chapter 5: AutoMoDe -- Chapter 6: A cross-domain method for generation of constructive and perturbative heuristics -- Chapter 7: Hyper-heuristics -- Chapter 8: Towards Real-time Federated Evolutionary Neural -- Chapter 9: Knowledge Transfer in Genetic Programming -- Chapter 10: Automated Design of Classification Algorithms -- Chapter 11: Automated Design (AutoDes)

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.

ISBN: 9783030720698$q(electronic bk.)

Standard No.: 10.1007/978-3-030-72069-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .A88 2021

Dewey Class. No.: 006.31

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520 $a 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.
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