Evolutionary Learning: Advances in Theories and Algorithms
Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolu
- PDF / 6,171,360 Bytes
- 361 Pages / 453.544 x 683.151 pts Page_size
- 74 Downloads / 268 Views
Evolutionary Learning Advances in Theories and Algorithms
Evolutionary Learning: Advances in Theories and Algorithms
Zhi-Hua Zhou Yang Yu Chao Qian •
•
Evolutionary Learning: Advances in Theories and Algorithms
123
Zhi-Hua Zhou Nanjing University Nanjing, Jiangsu, China
Yang Yu Nanjing University Nanjing, Jiangsu, China
Chao Qian Nanjing University Nanjing, Jiangsu, China
ISBN 978-981-13-5955-2 ISBN 978-981-13-5956-9 https://doi.org/10.1007/978-981-13-5956-9
(eBook)
© Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Preface
Around the year of 2001, the first author of this monograph, Zhou, developed with his collaborators a powerful selective ensemble approach. This approach is able to produce small-sized ensembles with generalization performance even stronger than that of full-sized ones by exploiting genetic algorithm, a popularly used evolutionary algorithm (EA). Zhou realized that EAs are powerful optimization techniques that can be well useful in various machine learning tasks. At that time, however, EAs were almost purely heuristic, not favored by the machine learning community with strong theoretical flavor. Impressed by the successes of EAs in applications, Zhou believed there must be some theoretical explanations behind their mysteries and decided to start this track of research. In 2004, the second author of this monograph, Yu, finished his bachelor thesis on selective ensemble under the supervision of Zhou. Yu then joined Zhou as a PhD student taking theoretical aspects of EAs as his thesis topic, and obtained PhD degree in 2011. In 2009, Zhou accepted the third author of this monograph, Qian, as his PhD s
Data Loading...
