Table Of ContentGENETIC ALGORITHMS
IN APPLICATIONS
Edited by Rustem Popa
Genetic Algorithms in Applications
Edited by Rustem Popa
Published by InTech
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Copyright © 2012 InTech
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First published March, 2012
Printed in Croatia
A free online edition of this book is available at www.intechopen.com
Additional hard copies can be obtained from [email protected]
Genetic Algorithms in Applications, Edited by Rustem Popa
p. cm.
ISBN 978-953-51-0400-1
Contents
Preface IX
Part 1 GAs in Automatic Control 1
Chapter 1 Selection of Optimal Measuring Points
Using Genetic Algorithm in the Process
to Calibrate Robot Kinematic Parameters 3
Seiji Aoyagi
Chapter 2 Model Predictive Controller Employing
Genetic Algorithm Optimization of Thermal
Processes with Non-Convex Constraints 19
Goran Stojanovski and Mile Stankovski
Chapter 3 Enhancing Control Systems
Response Using Genetic PID Controllers 35
Osama Y. Mahmood Al-Rawi
Chapter 4 Finite-Thrust Trajectory Optimization Using a Combination
of Gauss Pseudospectral Method and Genetic Algorithm 59
Qibo Peng
Chapter 5 Genetic Algorithm Application in Swing Phase
Optimization of AK Prosthesis with Passive
Dynamics and Biomechanics Considerations 71
Ghasem Karimi and Omid Jahanian
Part 2 GAs in Scheduling Problems 89
Chapter 6 Genetic Algorithms Application
to Electric Power Systems 91
Abdel-aal H. Mantawy
Chapter 7 Genetic Algorithms Implement in
Railway Management Information System 125
Jia Li-Min and Meng Xue-Lei
VI Contents
Part 3 GAs in Electrical and Electronics Engineering 149
Chapter 8 Efficient VLSI Architecture
for Memetic Vector Quantizer Design 151
Chien-Min Ou and Wen-Jyi Hwang
Chapter 9 Multiple Access System Designs
via Genetic Algorithm in Wireless Sensor Networks 169
Shusuke Narieda
Chapter 10 Genetic Algorithms in Direction Finding 185
Dario Benvenuti
Chapter 11 Applications of Genetic Algorithm
in Power System Control Centers 201
Camila Paes Salomon, Maurílio Pereira Coutinho,
Carlos Henrique Valério de Moraes, Luiz Eduardo Borges da Silva
Germano Lambert-Torres and Alexandre Rasi Aoki
Part 4 GAs in Pattern Recognition 223
Chapter 12 Applying Genetic Algorithm in
Multi Language’s Characters Recognition 225
Hanan Aljuaid
Chapter 13 Multi-Stage Based Feature Extraction Methods
for Uyghur Handwriting Based Writer Identification 245
Kurban Ubul, Andy Adler and Mamatjan Yasin
Chapter 14 Towards the Early Diagnosis of Alzheimer’s
Disease Through the Application of
a Multicriteria Classification Model 263
Amaury Brasil, Plácido Rogério Pinheiro
and André Luís Vasconcelos Coelho
Part 5 GAs in Trading Systems 279
Chapter 15 Portfolio Management Using Artificial
Trading Systems Based on Technical Analysis 281
Massimiliano Kaucic
Chapter 16 Genetic Algorithm Application for
Trading in Market toward Stable Profitable Method 295
Tomio Kurokawa
Preface
Genetic Algorithms (GAs) are global optimization techniques used in many real-life
applications. They are one of several techniques in the family of Evolutionary
Algorithms – algorithms that search for solutions to optimization problems by
“evolving” better and better solutions.
A Genetic Algorithm starts with a population of possible solutions for the desired
application. The best ones are selected to become parents and then, using genetic
operators like crossover and mutation, offspring are generated. The new solutions are
evaluated and added to the population and low-quality solutions are deleted from the
population to make room for new solutions. The members of the population tend to
get better with the increasing number of generations. When the algorithm is halted,
the best member of the existing population is taken as the solution to the problem.
Genetic Algorithms have been applied in science, engineering, business and social
sciences. A number of scientists have already solved many real-life problems using
Genetic Algorithms. This book consists of 16 chapters organized in five sections.
The first section contains five chapters in the field of automatic control. Chapter 1
presents a laser tracking system for measuring a robot arm’s tip with high accuracy
using a GA to optimize the number of measurement points. Chapter 2 presents a
model predictive controller that uses GAs for the optimization of cost function in a
simulation example of industrial furnace control. Chapter 3 describes a design method
to determine PID controller parameters using GAs. Finite-thrust trajectory
optimization using a combination between Gauss Pseudospectral Method and a GA is
proposed in Chapter 4. Chapter 5 describes the optimization of an above-knee
prosthesis physical parameters using GAs.
The next section of the book deals with scheduling of resources and contains two
chapters in this field. Chapter 6 analyzes the Unit Commitment Problem, that is the
problem of selecting electrical power systems to be in service during a scheduling
period and determining the length of that period. Chapter 7 deals with several typical
applications of GAs to solving optimization problems arising from railway
management information system design, transportation resources allocation and
traffic control for railway operation.
X Preface
The third section contains four chapters in the field of electrical and electronics
engineering. Chapter 8 proposes a new VLSI architecture which is able to implement
Memetic Algorithms (which can be viewed as the hybrid GAs) in hardware. Chapter 9
describes a distributed estimation technique that uses GA to optimize frequency and
time division multiple access which is employed in several wireless sensor networks
systems. Chapter 10 deals with the problem of direction of arrival estimation through
a uniform circular array interferometer. GAs have been compared with other
optimization tools and they have confirmed a more robust behavior when low
computing power is available. The last chapter in this section, Chapter 11, presents the
GA application in three functions commonly executed in power control centers: power
flow, system restoration and unit commitment.
The fourth section of the book has three chapters that illustrate two applications of
character recognition and a multi-criteria classification. Chapter 12 applies a GA for
offline handwriting character recognition. Chapter 13 deals with writer identification
by integrating GAs with several other known techniques from pattern recognition.
Chapter 14 proposes an early diagnosis of Alzheimer’s disease by combining a multi-
criteria classification model with a GA engine, with better results than those offered by
other existing methods.
Finally, the last section contains two chapters dealing with trading systems. Chapter
15 discusses the development of artificial trading systems for portfolio optimization
using a multi-modular evolutionary heuristic capable of dealing efficiently with the
zero investment strategy. Chapter 16 provides some insight into overfitting in the
environment of trading in market and proposes a GA application for trading in market
toward a stable profitable method.
These evolutionary techniques may be useful to engineers and scientists from various
fields of specialization who need some optimization techniques in their work and who
are using Genetic Algorithms for the first time in their applications. I hope that these
applications will be useful to many other people who may be familiarizing themselves
with the subject of Genetic Algorithms.
Rustem Popa
Department of Electronics and Telecommunications
“Dunarea de Jos” University of Galati
Romania
