Genetic Algorithms And Fuzzy Logic Systems Soft Computing Perspectives

252 Pages·1997·11.142 MB·English

by  Elie Sanchez

GENETIC HL60RITHM5 AND
FUZZV LOGIC SVSTEMS
Soft Computing Perspectives
ADVANCES IN FUZZY SYSTEMS — APPLICATIONS AND THEORY
Honorary Editor: Lotfi A. Zadeh (Univ. of California, Berkeley)
Series Editors: Kaoru Hirota (Tokyo Inst, of Tech.),
George J. Klir (Binghamton Univ.-SUNY),
Elie Sanchez (Neurinfo),
Pei-Zhuang Wang (West Texas A&M Univ.),
Ronald R. Yager (lona College)
Vol. 1: Between Mind and Computer: Fuzzy Science and Engineering
(Eds. P.-Z. Wang and K.-F. Loe)
Vol. 2: Industrial Applications of Fuzzy Technology in the World
(Eds. K. Hirota and M. Sugeno)
Vol. 3: Comparative Approaches to Medical Reasoning
(Eds. M. E. Cohen and D. L. Hudson)
Vol. 4: Fuzzy Logic and Soft Computing
(Eds. B. Bouchon-Meunier, R. R. Yager and L A. Zadeh)
Vol. 5: Fuzzy Sets, Fuzzy Logic, Applications
(G. Bojadziev and M. Bojadziev)
Vol. 6: Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers
by Lotfi A. Zadeh
(Eds. G. J. Klir and B. Yuan)
Vol. 7: Genetic Algorithms and Fuzzy Logic Systems: Soft Computing
Perspectives
(Eds. E. Sanchez, T. Shibata and L A. Zadeh)
Vol. 8: Foundations and Applications of Possibility Theory
(Eds. G. de Cooman, D. Ruan and E. E. Kerre)
Vol. 10: Fuzzy Algorithms: With Applications to Image Processing and
Pattern Recognition
(Z. Chi, H. Yan and T. D. Pham)
Vol. 11: Hybrid Intelligent Engineering Systems
(Eds. L. C. Jain and R. K. Jain)
Forthcoming volumes:
Vol. 9: Fuzzy Topology
(Y. M. Liu and M. K. Luo)
Vol. 12: Fuzzy Logic for Business, Finance, and Management
(G. Bojadziev and M. Bojadziev)
Vol. 13: Fuzzy and Uncertain Object-Oriented Databases: Concepts and Models
(Ed. R. de Caluwe)
Vol. 14: Automatic Generation of Neural Network Architecture Using
Evolutionary Computing
(Eds. E. Vonk, L C. Jain and R. P. Johnson)
Advances in Fuzzy Systems — Applications and Theory Vol. 7
GENETIC ALGORITHMS ID
FUZZV LOGIC SYSTEMS
Soft Computing Perspectives
Editors
Elie Sanchez
Universite de la Mediterrannee, Aix-Marseille II, France
Takanori Shibata
MIT, USA & Mechanical Engineering Laboratory, MITI, Japan
Lotfi A. Zadeh
BISC, University of California, Berkeley, USA
©World Scientific
!• Singapore • New Jersey • London • Hong Kong
Published by
World Scientific Publishing Co. Pte. Ltd.
P O Box 128, Farrer Road, Singapore 912805
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Library of Congress Cataloging-in-Publication Data
Genetic algorithms and fuzzy logic systems: soft computing perspectives /
editors, Elie Sanchez, Takanori Shibata, Lotfi A. Zadeh
p. cm. — (Advances in fuzzy systems : vol. 7)
Includes bibliographical references.
ISBN 9810224230
1. Genetic algorithms. 2. Fuzzy logic.
I. Sanchez, Elie, 1944- . II. Shibata, Takanori, 1967-
III. Zadeh, Lotfi Asker. IV. Series.
QA402.5.G4535 1997
006'.3-dc21 96-51850
CIP
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
Copyright © 1997 by World Scientific Publishing Co. Pte. Ltd.
All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means,
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Printed in Singapore by UtoPrint
FOREWORD
One of my motivations for working on the Handbook of Genetic Algorithms in
1990 was to demonstrate the real-world power of evolutionary algorithms. We
have certainly come a long way since then. Now that evolutionary algorithms
are well-known as optimization techniques, I believe that what is important is
to communicate the range of problems to which they can be applied. This vol
ume displays the power of evolutionary algorithms when combined with fuzzy
logic. These are exciting times in the fields of fuzzy logic and evolutionary
algorithms, and this book will add to the excitement, because it is the first
volume to focus on the growing connections between the fields of evolutionary
algorithms and fuzzy logic. These two fields have been maturing for a long
time, and both have had impressive real-world impact. The ability of fuzzy
logic systems to capture the spirit of human rules and to express the sort of
gradual predicates that we humans often work with has led to a variety of
expert systems and industrial control systems that are easily formulated and
understood, and that produce marvelously humanlike behavior. The ability
of evolutionary algorithms across a variety of domains to produce better solu
tions than those we find using mathematical or heuristic techniques is similarly
surprising and marvelous.
What is at the heart of this volume is a fact that is not so well known.
Evolutionary algorithms can be used to create, modify, improve, and update
fuzzy logic systems. The papers in this volume show how these two techniques
can be combined — perhaps with humans generating the initial fuzzy logic
rules or predicates and with evolutionary algorithms working to evolve rule
sets, modify the set membership parameters, or update the fuzzy logic systems
as the domain changes. When these techniques are combined in this way, the
results can be better than if either is applied to the problem alone.
Although fuzzy logic and evolutionary algorithms have been profitably
combined and applied to real-world problems, these approaches are not guar
anteed to provide viable solutions. Experience and an understanding of the
mechanics of each discipline are required in order to determine whether they
should be applied to a specific problem and, if so, where they should be applied
and how. This book will be a valuable aid to anyone considering the appli
cation of fuzzy logic and evolutionary algorithms to real problems, because it
contains a number of detailed accounts of such applications written by authors
in several countries. By making these accounts available in one place, the edi
tors of this book have made it much easier for us to benefit from the authors'
experience, and have done us a great service.
Lawrence Davis, President
Tica Associates
Cambridge, Massachusetts
Editor of Handbook of Genetic Algorithms
v
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PREFACE
The past few years have witnessed a rapid growth in the number and variety
of applications of fuzzy logic, ranging from consumer products and industrial
process control to medical instrumentation, information systems, and decision
analysis. The foundations of fuzzy logic have become firmer and its impact
within the basic sciences has become more visible and more substantive.
In a wide sense, fuzzy logic is more or less synonymous with the theory of
fuzzy sets, i.e., a theory of classes with unsharp boundaries. It is in this sense
that any field X can be fuzzified — and hence generalized — by replacing the
concept of a crisp set in X by a fuzzy set. What is gained through fuzzification
is greater generality, higher expressive power, and enhanced ability to model
real-world problems.
The notion of soft computing has emerged recently; it concerns modes
of computing in which imprecision is traded for tractability, robustness, ease
of implementation, and lower solution cost. The main components of soft
computing are fuzzy logic, neural networks, probabilistic reasoning, genetic
algorithms, chaos theory, and parts of learning theory. A natural evolution in
soft computing has been the emergence of hybrid systems, which is of particular
interest when combining fuzzy logic with neural networks or, more recently,
with genetic algorithms.
Genetic algorithms are search algorithms based on the mechanics of natu
ral selection and natural genetics; they use a computationally simulated version
of survival of the fittest, acting on string structures. Genetic algorithms are
iterative procedures which maintain a population of candidate solutions to op
timize a fitness function. The fitness function, an evaluation function in a
search problem, can be nonlinear and discontinuous. Genetic algorithms are
not based on gradient search and they can avoid local minima problems: they
search large spaces efficiently without the need of derivative information. They
have been applied to several classes of search and optimization problems, with
impressive results.
The papers presented in this volume address applications combining fuzzy
logic and genetic algorithms in soft computing perspectives. Applications of
these combined fields cover fuzzy logic control (helicopter flight, intelligent
robots, active suspension; cell recognition; level of water tanks; ball-and-beam);
neural networks and fuzzy inference (function approximation, fuzzy classifi
cation, and medical diagnosis; fraud detection); information retrieval (query
optimization); interactive genetic algorithm (cartoon face search); cognitive
feedback (medical diagnosis). Furthermore, an extensive annotated bibliogra
phy of fuzzy logic-genetic algorithms is included.
Elie Sanchez, Takanori Shibata, and Lotfi A Zadeh
Editors
vn
ACKNOWLEDGMENTS
The editors should like to express their appreciation to Mr. Yew Kee Chiang of
World Scientific Publishing Co. for his continuous support in the preparation
of this volume.
CONTENTS
Foreword v
L. Davis
Preface vii
E. Sanchez, T. Shibata, and L. A. Zadeh
Helicopter Flight Control with Fuzzy Logic and Genetic Algorithms 1
C. Phillips, C. L. Karr, and G. W. Walker
Skill Acquisition and Skill-Based Motion Planning for
Hierarchical Intelligent Control of a Redundant Manipulator 19
T. Shibata
A Creative Design of Fuzzy Logic Controller Using a Genetic Algorithm 37
T. Hashiyama, T. Furuhashi, and Y. Uchikawa
Automatic Fuzzy Tuning and Its Applications 49
H. Ishigami, T. Fukuda, and T. Shibata
An Evolutionary Algorithm for Fuzzy Controller Synthesis and
Optimization Based on SGS-Thomson's W.A.R.P. Fuzzy Processor 71
R. Poluzzi, G. G. Rizzotto, and A. G. B. Tettarnanzi
On-Line Self-Structuring Fuzzy Inference Systems for Function
Approximation 91
H. Bersini
Fuzzy Classification Based on Adaptive Networks and
Genetic Algorithms 113
C.-T. Sun and J.-S. Jang
Intelligent Systems for Fraud Detection 133
J. Kingdon
Genetic Algorithms for Query Optimization in Information
Retrieval: Relevance Feedback 155
D. H. Kraft, F. E. Petry, B. P. Buckles, and T. Sadasivan
Fuzzy Fitness Assignment in an Interactive Genetic Algorithm
for a Cartoon Face Search 175
K. Nishio, M. Murakami, E. Mizutani, and N. Honda