GENETIC ALGORITHMS IN APPLICATIONS pptx

GENETIC ALGORITHMS IN APPLICATIONS Edited by Rustem Popa Genetic Algorithms in Applications Edited by Rustem Popa Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. As for readers, this license allows users to download, copy and build upon published chapters even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Marina Jozipovic Technical Editor Teodora Smiljanic Cover Designer InTech Design Team 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 orders@intechopen.com 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 . GENETIC ALGORITHMS IN APPLICATIONS Edited by Rustem Popa Genetic Algorithms in Applications Edited by Rustem Popa Published by InTech Janeza Trdine 9, 51000. 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. place of the online manual teaching (Mooring et al., 1991). In the offline teaching, the joint angles to achieve the given Cartesian position of the arm’s tip are calculated using a kinematic model