CLASSICAL AND EVOLUTIONARY
ALGORITHMS IN THE OPTIMIZATION
OF OPTICAL SYSTEMS
Genetic Algorithms and
Evolutionary Computation
Consulting Editor, David E. Goldberg
University of Illinois at Urbana-Champaign
[email protected]
Additional titles in the series:
Efficient and Accurate Parallel Genetic Algorithms, Erick Cantu-Paz ISBN: 07923-7221-2
Estimation of Distribution Algorithms: A New Tool for Evolutionary
Computation, edited by Pedro Larranaga, Jose A. Lozano ISBN: 0-7923-7466-5
Evolutionary Optimization In Dynamic Environments, Jurgen Branke
7923-7631-5
Anticipatory Learning Classifier Systems, Martin V. Butz
ISBN: 0-
ISBN: 0-7923-7630-7
Evolutionary Algorithms for Solving Multi-Objective Problems, Carlos A. Coello
Coello, David A. Van Veldhuizen, and Gary B. Lamont ISBN: 0-306-46762-3
OmeGA: A Competent Genetic Algorithm for Solving Permutation and
Scheduling Problems, Dimitri Knjazew ISBN: 0-7923-7460-6
The Design of Innovation: Lessons from and for Competent Genetic
Algorithms, David E. Goldberg ISBN: 1-4020-7098-5
Noisy Optimization with Evolution Strategies, Dirk V. Arnold ISBN: 1-40207105-1
Genetic Algorithms and Evolutionary Computation publishes research monographs, edited
collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include
the theory, implementation, and application of genetic algorithms (GAs), evolution strategies (ESs),
evolutionary programming (EP), learning classifier systems (LeSs) and other variants of genetic and
evolutionary computation (GEe). Proposals in related fields
such as artificial life, adaptive behavior, artificial immune
systems, agent-based systems, neural computing, fuzzy
systems, and quantum computing will be considered for
Genetic Algorithms and
publication in this series as long as GEe techniques are part
Evolutionary Computation
of or inspiration for the system being described. Manuscripts
describing GEe applications in all areas of engineering,
commerce, the sciences, and the humanities are encouraged. http://www.wkap.nl/prod/s/GENA
GENAGENAGENA
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CLASSICAL AND EVOLUTIONARY
ALGORITHMS IN THE OPTIMIZATION
OF OPTICAL SYSTEMS
by
Darko Vasiljevic
Military Technical Institute, Yugoslavia
Military Academy, Yugoslavia
Mechanical Engineering Faculty, University of Belgrade, Yugoslavia
KLUWER ACADEMIC PUBLISHERS
Boston I Dordrecht I London
Distributors for North, Central and South America:
Kluwer Academic Publishers
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Telephone 31 786 576 000
Fax 31 786576474
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Library of Congress Cataloging-in-Publication Data
Vasiljevic, Darko, 1960Classical and evolutionary algorithms in the optimization of optical systems I by Darko
Vasiljevic.
p. cm. -- (Genetic Algorithms and Evolutionary Computation; 9)
Includes bibliographical references and index.
ISBN: 1-4020-7140-X (alk. paper)
1. Optical instruments--Design and construction. 2. Lenses--Design and construction.
3.0ptics--Mathematics. 4. Genetic algorithms. I. Title. II. Series.
QC372.2.D4 V37 2002
681' 4--dc21
2002069474
Copyright © 2002 by Kluwer Academic Publishers
All rights reserved. No part of this work may be reproduced, stored in a retrieval system, or
transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming,
recording, or otherwise, without the written permission from the Publisher, with the exception of
any material supplied specifically for the purpose of being entered and executed on a computer
system, for exclusive use by the purchaser of the work.
Permission for books published in Europe: [email protected]
Permissions for books published in the United States of America: [email protected]
Printed on acid-free paper.
To Rada
Contents
Preface
•••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• IX
Acknowledgments••••••••.••••••••.•••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••.•••••• xiii
Chapter 1
Chapter 2
Introduction.•..•.•.•.•.••.•.••..••••••.•••.••.•....•..•.•.••.•..••••.•••.•••••••••••••••••••••••••• 1
Classical algorithms in the optimization
of optical systems ............................................................................. 11
Chapter 3
Genetic algorithms ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 41
Chapter 4
Evolution strategies•••••••••••••••••••••••••••.••••.••••.••.•.••••.•••.•.•••.•••..•••...•.•.• 69
ChapterS
Comparison of optimization algorithms ........................................ 83
Chapter 6
Ray trace .••••.•••..••.•.•.••••.•.•••.•••••••••••••••••••.••••••••••••••••••••••••••••••••••••••.•.• 89
Chapter 7
Aberrations ••••••.••••••••••••.•.•.••.•••.•••••..•.•..•.••.••••.••••••••.•••••.•••.••••••••••••• 101
Chapter 8
Damped least squares optimization implementation ••..•••.•.•....... 119
Chapter 9
Adaptive steady-state genetic algorithm
implementation •••••••••••••••••..•••••.•..•••.••.•••••.••••.•••••••••••••••••••••••••••.•••• 137
Chapter 10 Two membered evolution strategy
ES EVOL implementation ............................................................ 155
Chapter 11 Multimembered evolution strategies ES GRUP and
ES REKO implementation .•••••.•••..•••..•••....•..••••...•••.•.••.••••••••••••••••• 165
Chapter 12 Multimembered evolution strategy
ES KORR implementation •••••••••••••••••••••••••.••••.•••.•••••••.••••••••.•••••••• 175
Chapter 13 The Cooke triplet optimizations ................................................... 187
Chapter 14 The Petzval objective optimizations ............................................. 213