Evolutionary Optimization Algorithms

【实例简介】
Evolutionary Optimization Algorithms Wiley Evolutionary Optimization Algorithms Wiley Evolutionary Optimization Algorithms Wiley
EVOLUTIONARY OPTIMIZATION ALGORITHMS EVOLUTIONARY OPTIMIZATION ALGORITHMS Biologically-Inspired and Population-Based Approaches to Computer Intelligence Dan simon Cleveland State University WILEY Copyright o 2013 by John Wiley Sons, Inc. All rights reserved Published by John Wiley sons, Inc, Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as ermitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc, 222 Rosewood Drive, Danvers, MA 01923, (978)750-8400, fax (978)750-4470,oronthewebatwww.copyright.comRequeststothePublisherforpermissionshould be addressed to the permissions department, John wiley sons, Inc., 1 1l River Street, Hoboken, NJ 07030,(201)748-6011,fax(201)748-6008,oronlineathttp://www.wiley.com/go/permission Limit of liability/Disclaimer of warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages For general information on our other products and services or for technical support, please contact or Customer Care Department within the United States at(800)762-2974, outside the United States at (317)572-3993 or fax(317)572-4002 wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com Library of congress Cataloging-in-Publication Data is available. ISBN978-0-470-93741-9 Printed in the United States of america 10987654321 SHORT TABLE OF CONTENTS Part I: Introduction to Evolutionary Optimization 1 Introduction optimization Part II: Classic Evolutionary Algorithms 3 Genetic algorithms 4 Mathematical Models of Genetic Algorithms 3 5 Evolutionary Programming 95 6 Evolution Strategies 117 7 Genetic Programming 8 Evolutionary algorithm Variations 179 Part III: More Recent Evolutionary algorithms 9 Simulated Annealing 223 10 Ant Colony optimization 241 11 Particle Swarm Optimization 265 12 Differential Evolution 293 13 Estimation of Distribution Algorithms 313 14 Biogeography-Based Optimization 351 15 Cultural algorithms 377 16 Opposition-Based Learning 397 17 Other Evolutionary Algorithms 421 Part IV: Special Types of Optimization Problems 18 Combinatorial Optimization 449 19 Constrained Optimization 481 20 Multi-Objective optimization 517 21 Expensive, Noisy, and Dynamic Fitness Functions 563 Appendices a Some Practical Advice 607 B The No Free Lunch Theorem and Performance Testing 613 c Benchmark Optimization Functions 641 DETAILED TABLE OF CONTENTS A Cknowledgments A cronyms x11 List of algorithms XXVI PART INTRODUCTION TO EVOLUTIONARY OPTIMIZATION 1 Introduction 1.1Te 1.2 Why Another Book on Evolutionary Algorithms? 1.3 Prerequisites 1.4 Homework problems 1.5 Notation 1.6 Outline of the book 2455678 1.7 A Course Based on This book 2 Optimization 11 2.1 Unconstrained Optimization 2.2 Constrained Optimization 15 2.3 Multi-Objective Optimization 16 2.4 Multimodal optimization 2.5 Combinatorial Optimization III DETAlLED TABLE OF CONTENTS 2.6 Hill Climbing 2.6.1 Biased Optimization algorithms 25 2.6.2 The Importance of monte Carlo simulations 26 2.7 Intelligence 26 2.7.1 Adaptation 2.7.2 Randomness 27 2.7.3 Communication 2.7.4 Feedback 28 2.7.5 Exploration and Exploitation 2.8 Conclusion Problems 30 PART I CLASSIC EVOLUTIONARY ALGORITHMS 3 Genetic algorithms 35 3. 1 The History of Genetics 3.1.1 Charles darwin 36 3.1.2 Gregor Mendel 3.2 The science of genetics 3.3 The History of Genetic algorithms 41 3.4 A Simple Binary Genetic algorithm 44 3. 4. 1 A Genetic Algorithm for Robot design 44 3.4.2 Selection and Crossover 45 3.4.3 Mutation 49 3. 4.4 GA Summar 49 3.4.5 GA Tuning Parameters and Examples 3.5 A Simple Continuous genetic algorithm 3.6 Conclusion 59 Problems 4 Mathematical Models of genetic algorithms 63 4.1 Schema Theory 64 4.2 Markov Chains 68 4.3 Markov Model Notation for Evolutionary Algorithms 73 4.4 Markov Models of Genetic algorithms 4.4.1 Selection 76 4.4.2 Mutation 4.4.3 Crossover 4.5 Dynamic System Models of genetic algorithms 4.5.1 Selection 82 4.5.2 Mutation 4.5.3 Crossover 【实例截图】
【核心代码】