Variants of Evolutionary Algorithms for Real-World Applications
Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interes
- PDF / 12,724,169 Bytes
- 470 Pages / 429.725 x 659.895 pts Page_size
- 0 Downloads / 185 Views
Raymond Chiong, Thomas Weise, and Zbigniew Michalewicz (Eds.)
Variants of Evolutionary Algorithms for Real-World Applications
ABC
Editors Raymond Chiong Faculty of ICT Swinburne University of Technology Melbourne, VIC 3122, Australia E-mail: [email protected]
Zbigniew Michalewicz School of Computer Science University of Adelaide Adelaide, SA 5005, Australia E-mail: [email protected]
Thomas Weise Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology University of Science and Technology of China (USTC) Hefei 230027, Anhui, China E-mail: [email protected]
ISBN 978-3-642-23423-1
e-ISBN 978-3-642-23424-8
DOI 10.1007/978-3-642-23424-8 Library of Congress Control Number: 2011935740 c 2012 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, 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. Typeset & Cover Design: Scientific Publishing Services Pvt. Ltd., Chennai, India. Printed on acid-free paper 987654321 springer.com
Preface
Started as a mere academic curiosity, Evolutionary Algorithms (EAs) first came into sight back in the 1960s. However, it was not until the 1980s that the research on EAs became less theoretical and more practical. As a manifestation of population-based, stochastic search algorithms that mimic natural evolution, EAs use genetic operators such as crossover and mutation for the search process to generate new solutions through a repeated application of variation and selection. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. The general-purpose, black-box character of EAs makes them suitable for a wide range of realworld applications. Standard EAs such as Genetic Algorithms (GAs) and Genetic Programming (GP) are becoming more and more accepted in the industry and commercial sectors. With the dramatic increase in computational power today, an incredible diversification of new application areas of these techniques can be observed. At the same time, variants and other classes of evolutionary optimisation methods such as Differential Evolution, Estimation of Distribution Algorithms, Co-evolutionary Algorithms and Multi-Objecti
Data Loading...
