Introduction to Evolutionary Algorithms

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Introduction to Evolutionary Algorithms,进化算法电子书
Series editor Professor Rajkumar Roy Department of Enterprise Integration School of Industrial and Manufacturing Science Cranfield University Cranfield Bedford MK43 OAL UK Other titles published in this series Cost Engineering in Practice John mcilwraith IPA- Concepts and Applications in Engineering Jerzy pokojski Strategic Decision Making Navneet bhushan and Kanwal rai Product Lifecycle Management John stark From Product Description to Cost: A Practical Approach Volume 1 The Parametric Approach Pierre fussier From Product Description to Cost: A Practical approach Volume 2: Building a Specific Model Pierre fussier Decision-Making in Engineering design Yotaro hatamura Composite Systems Decisions Mark sh. levin Intelligent Decision-making Support Systems Jatinder N D. Gupta, Guisseppi A Forgione and Manuel Mora t Knowledge acquisition in Practice N.R. Milton Global Product: Strategy, Product Lifecycle Management and the billion Customer Question John Stark Enabling a Simulation Capability in the organisation Andrew Greasley Network Models and optimization Mitsuo gen runewei Cheng and Lin lin Management of Uncertainty Gudela grote Xinjie yu· Mitsuo gen Introduction to Evolutionary algorithms Springer Xinjie Yu, PhD Mitsuo gen phD Department of Electrical Engineering Fuzzy Logic Systems Institute(FLSI) Tsinghua University 680-41 Kawazu 100084 Beijing 820-0067 lizuka China Japan yuxj@@tsinghua.edu.cn gen@flsi or jp ISSN16195736 ISBN978-1-84996-128-8 e-ISBN978-1-84996-1295 DOⅠ10.10071978-1-84996-129-5 Springer London Dordrecht Heidelberg New York British library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2010929767 o Springer-Verlag London limited 2010 Discipulus is a trademark of RML Technologies, 7606 S Newland St, Littleton, CO 80128, USA, stered trademark of wolf h. Inc. 100 Trade Center dri, ChampaignIl61820-7237,usa,www.wolfram.com MATLAB is a registered trademark of The Math Works, Inc., 3 Apple Hill Drive, Natick, MA, 01760-2098Usa.www.mathworks.com Apart from any fair dealing for the purposes of research or private study, or criticism or review,as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers The use of 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 laws and regulations and therefore al ee for general use The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made Cover design: eStudio calamar, Figueres/Berlin Printed on acid-free paper SpringerispartofSpringerScience+businessMedia(www.springer.com) To our families, friends, and students Preface This is a textbook on evolutionary algorithms (EAs). In preparing the proposal and the manuscript, the following questions were always kept in our minds Is this book convenient for teaching, studying, and self-study, i.e., have the con tents been arranged in a pedagogically sound way? Does this book introduce the state of the art of eas? Does this book cover the contents as comprehensively as possible? Does this book contain specific programming codes so that the reader can use them directly? For the first two questions, we would like to say yes. We want this textbook to be intuitive because intuitive ideas, and not strict proofs lead to the kind of innovation we seek. We also want to build a bridge connecting the basics and the cutting edge so that our students can reach the peak quickly yet with a solid grasp of the mate- rial. Our answer to the remaining two questions is no This textbook is neither an encyclopedia nor a cookbook. We selected only interesting and important topics and left the reader to implement the codes for the algorithms after we have deliberately removed all understanding obstacles. The reason for the latter consideration lies in the belief contained in the following saying: Tell me and I'lI forget; show me and I may remember; let me try, and I can understand EAs have attracted considerable interest in recent years To understand how " hot the topic is, consider the number of papers indexed by the Science Citation Index (SCI) every year. If the reader has the access to the sCl, he/she is encouraged to do a search to verify the hotness of this topic The reason the topic is so hot is mainly because of its effects on various problems, i. e, it really works. We will introduce various application examples to show how simple ideas can be expanded to solve complex problems The procedure of designing or analyzing an algorithm for solving optimization or learning problems is full of challenges, 1. e, problems are difficult. This is irrele vant. We will accompany and assist the reader when necessary we will demonstrate the basic ideas behind the scary equations and try our best to make things easy to understand. Incidentally, this cumbersome procedure is also interesting. Before and during the procedure of climbing Mount Fuji, we thought it would be torture while at the summit, we felt that all the sufferings were interesting The prerequisites of this book lie in two aspects. In the mathematics part, a basic understanding of linear algebra, multivariable calculus, probability, and statistics i necessary. The other demand is derived from a proficiency in programming. We assume a firm grasp of at least one programming language, i.e., you can implement an algorithm Since these requirements are in the common curriculum of junior undergraduate programs, senior undergraduate or graduate students should be able to read this textbook without knowledge obstacles The pedagogical approaches used in this text can be summarized as follows Building the mansion from the bricks. We will always focus on the key elements of an algorithm because later they might become your tools From specific to general. We will always discuss specific simple examples before formal expressions. From idea to implementation. We will always introduce the initial notions before discussing the concrete contents Explaining the critical part but leaving questions unanswered deliberately. We will leave some obstacles deliberately in the context to activate your thoughts R W. Hamming gave his motto as the following statement: The purpose of computing is insight, not numbers"in his famous book Numerical Methods for Sci entists and engineers. here we would like to use it again to represent our own atti- tude toward EAs. As algorithm designers, we care more about the solution landscape of the problem and the corresponding search ability of the algorithms, although we do seek the optimal solution to the problem. From this perspective, there will be less"how-to"in this textbook for specific instructions. On the contrary, there will be plenty of why-tos to explain the insights and the rationale of a given algorithm so that one can generate ones own cookbook according to these "why-tos. We believe that's what the reader wants Other teaching and self-study considerations include plenty of footnotes for men- tioning easily ignored yet important points, Suggestions for Further Reading" sec tions in most chapters for further study, exercises with various degrees of difficult to deepen one's understanding and even promote ones own"small-scale research The pedagogical relationship among the ten chapters of this textbook is illus- trated by Fig. 0. 1, where the dotted circles separate the ten chapters into three cat- egories: the basics, optimization problems, and other branches. The solid lines are the suggested pedagogical path for teaching or self-study. Chapters 1 to 3 are all necessary for subsequent chapters. After that, readers can skip around depending on personal interests. Dashed lines represent the improvements, e.g., an understanding I Knowledge of operations research, e.g., linear programming, nonlinear programming, and com binatorial optimization, might contribute to ones understanding but not be necessary 2 Father of the Hamming distance, which will be used often in this textbook 3 These terms will be explained explicitly and used throughout the book. here they can be taken as the procedure to find the optimal solution Preface of Chap. 4 might contribute to, but not be a prerequisite of, understanding Chap 6 and vice versa. We hope Fig 0.1 helps the reader form a mental map for different branches and applications of EAs Part I: EAs ChI: Introduction Genetic algorithm Ch2: Simple EAs Evolutionary programmin Simplex search Scatter search Ch3: Advanced eas Differential evolution Ch4: Constrained intelligence Ant colony optimization Ch5: Multimodal Particle swarm optimization Ch: Artificial Ch6: Multiobiective Immune system Ch10: genetie Ch7: Combinatorial programmIng Part 2: Problems Part 3: Others Fig. 0.1 The pedagogical relationship of the ten chapters in this textbook This book is the result of the development of the course evolutionary computa- tion and Its Applications given by the first author at Tsinghua University since 2002 and the numerous intensive courses taught by the second author around the world Too many names require mention in the acknowledgments. But we wish to express our appreciation for our students first, especially those in the seminar Evolutionary Algorithms 2010. Without your critical comments during the seminar, we would have no appreciation of the readers position, which is perhaps the most distinctive feature of this textbook. Many of old friends gave encourage and comments on the book, including Runwei Cheng, Baoding Liu, Kap Hwan Kim, etc. The fast response and the professional skill of Claire Protherough at Springer and Nadja Kroke at le- tex impressed us deeply. The final, but most important, acknowledgement belongs to Lin Wang and eiko gen because you are the backbones of our lives Beijing China Xinjie Yu Kitakyushu Japan, Mitsuo gen May 2010 Contents Part I Evolutionary Algorithms 1 Introduction 1.1 What Are Evolutionary algorithms Used For? 1. 2 What Are Evolutionary Algorithms? Suggestions for Further Reading References 2 Simple Evolutionary algorithms 2.1 Introductory Remarks 11 2.2 Simple genetic algorithm ....14 2.2.1 An Optimization problem ....14 2.2.2 Representation and evaluation 2.2.3 Initialization 2.2.4 Selection 18 2.2.5 Variation Operators 2.2.6 Simple genetic Algorithm Infrastructure 2.3 Evolution Strategy and Evolutionary Programming 2.3.1 Evolution Strategy 2.3.2 Evolutionary Programming 2.4 Direction-based Search 28 2. 4.1 Deterministic Direction-based search 2.4.2 Random Direction-based Search ummary S tions for further read ing Exercises and Potential Research Projects References 3 Advanced Evolutionary algorithms 39 3.1 Problems We Face ..39 3.2 Encoding and operato

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