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Evolutionary Intelligence

S. Sumathi · T. Hamsapriya · P. Surekha Evolutionary Intelligence An Introduction to Theory and Applications with Matlab

S. Sumathi T. Hamsapriya P. Surekha PSG College of Technology Coimbatore India ISBN: 978-3-540-75158-8 e-ISBN: 978-3-540-75382-7 Library of Congress Control Number: 2007938318 c 2008 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover Design: Erich Kirchner, Heidelberg Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com

Preface EVOLUTIONARY INTELLIGENCE: Theoretical Advances and Applications in Evolutionary Algorithms, Genetic Algorithms, Genetic Programming and Parallel Genetic Algorithms is intended to help, provide basic information, and serve as a ﬁrst straw for individuals, who are stranded in the mind boggling universe of Evo- lutionary Computation (EC). Over the course of the past years, global optimization algorithms imitating certain principles of nature have proved their usefulness in var- ious domains of applications. Especially worth learning are those principles where nature has found “stable islands” in a “turbulent ocean” of solution possibilities. Such phenomena can be found in annealing processes, central nervous systems and biological Evolution, which in turn have lead to the following optimization meth- ods: Simulated Annealing (SA), Artiﬁcial Neural Networks (ANNs) and the ﬁeld of Evolutionary Computation (EC). EC may currently be characterized by the following pathways: Genetic Algo- rithms (GA), Evolutionary Programming (EP), Evolution Strategies (ES), Classi- ﬁer Systems (CFS), Genetic Programming (GP), and several other problem solving strategies, that are based upon biological observations, that date back to Charles Darwin’s discoveries in the 19th century: the means of natural selection and the sur- vival of the ﬁttest, and theories of evolution. The inspired algorithms are thus termed Evolutionary Algorithms (EA). Despite decades of work in evolutionary algorithms, there remains a lot of un- certainty as to when it is beneﬁcial or detrimental to use recombination or mutation. This book provides a characterization of the roles that recombination and muta- tion play in evolutionary algorithms. Primary areas of coverage include the theory, implementation, and application of genetic algorithms (GAs), genetic programming (GP), parallel genetic algorithm (PGAs) and other variants of genetic and evolution- ary computation. This book is ideal for researchers, engineers, computer scientists, graduate students who consider what frontiers await their exploration. v

vi About the Book Preface This book gives a good introduction to evolutionary computation for those who are ﬁrst entering the ﬁeld and are looking for insight into the underlying mechanisms behind them. Emphasizing the scientiﬁc and machine learning applications of ge- netic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The author includes excellent problem sets, these being divided up into “thought exercises” and “com- puter exercises” in genetic algorithm. Practical use of genetic algorithms demands an understanding of how to implement them, and the author does so in the last two chapters of the book by giving the applications in various ﬁelds. This book also out- lines some ideas on when genetic algorithms and genetic programming should be used, and this is useful since a newcomer to the ﬁeld may be tempted to view a ge- netic algorithm as merely a fancy Monte Carlo simulation. The most difﬁcult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this. Various “exotic” approaches to improve the performance of genetic algorithms are also discussed such as the “messy” genetic algorithms, adaptive genetic algorithm and hybrid genetic algorithm. Salient Features ary Algorithms, Genetic Algorithm and Genetic Programming The salient features of this book includes, Detailed description on Evolutionary Computation Concepts such as Evolution- A thorough understanding of Parallel Genetic Algorithm and its models Worked out examples using MATLAB software Application case studies based on MATLAB in various ﬁelds like Control Sys- tems, Electronics, Image Processing, Power Electronics, Signal Processing, Com- munication and VLSI Design. MATLAB Toolboxes for Evolutionary Computation, Research Projects, Genetic Algorithm Codes in ‘C’ language, Abbreviations EC class/code libraries and software kits & Glossary. Organization of the Book Chapter 1 describes the history of evolutionary computation in brief and discusses the biological and artiﬁcial evolutionary process. The basic principle of evolution – Darwin’s theory of evolution is described in detail in this chapter. A note on Genetics and the four important paradigms of EC are discussed in detail. An introduction to

Preface vii global optimization and global optimization techniques such as Branch and Bound, Hybrid models, Tabu search etc., are elaborated in this chapter. Chapter 2 provides an insight into the principles of evolutionary algorithms and its structure. This chapter also elaborates on the components of EAs as representa- tion, ﬁtness function, population initialization, selection, recombination, mutation, reinsertion and reproduction. EAs often take less time to ﬁnd the optimal solution than gradient methods. However, most real-world problems involve simultaneous optimization of several often mutually concurrent objectives. Multi-objective EAs are able to ﬁnd optimal trade-offs in order to get a set of solutions that are optimal in an overall sense. This chapter also discusses Multi-objective optimization using evolutionary algorithms. Chapter 3 deals with the evolution of GA along with the operational functionali- ties of basic GA. The schema theorem is described with suitable illustrations. Some of the advanced operators that are used in GA are discussed and the types of GA such as parallel, sequential, hybrid, adaptive etc., are discussed in this chapter. The chapter also provides a set of basic MATLAB illustrations along with suitable codes. In chapter 4, a brief history of genetic programming is discussed. To get an idea about programming a basic introduction to Lisp Programming Language is dealt. The basic operations of GP are discussed along with an illustration. The steps of GP are also illustrated along with a ﬂow chart and enhanced versions of GP such as Meta GP, Cartesian GP and Strongly Typed GP are also elaborated in this chapter. Chapter 5 deals with Parallel GA and an overview of parallel and distributed computer architecture. The classiﬁcation of PGA and the various models are ex- plained in detail with necessary illustrations. Communication topologies play a vital role in the classiﬁcation of PGA. The advantages of PGA are also delineated. Chapter 6 presents the applications of EA in the area of Biometric processing, engineering design, grammar development, waveform synthesis, scheduling earth observing satellites and portfolio optimization for general risk measures. Chapter 7 presents the recent approaches of Genetic Algorithm with application examples in the areas of control systems, electronics, image processing, power elec- tronics, signal processing, VLSI and Communication Systems. Chapter 8 illustrates the application examples based on Genetic Programming in the areas of robotics, biomedical, physical science, interprocess communication, civil engineering, process control systems and artiﬁcial intelligence. Chapter 9 describes the Parallel GA application in timetable problem solving, as- sembling DNA Fragments, solving Job Shop Scheduling Problems, Graph Coloring Problems and Ordering Problems. About the Authors The Author Dr.S. Sumathi born on 31.01.1968 completed B.E Degree in Electron- ics and Communication Engineering and Masters Degree in Applied Electronics at Government College of Technology, Coimbatore, TamilNadu. The Author got Ph.D.

viii Preface Degree in the area of Data Mining currently, working as Assistant Professor in the Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore with teaching and research experience of 16 years. The Author received the prestigious Gold Medal from the Institution of Engi- neers Journal Computer Engineering Division - Subject Award for the research paper titled, “Development of New Soft Computing Models for Data Mining” 2002-2003 and also Best project award for UG Technical Report titled, “Self Or- ganized Neural Network Schemes: As a Data mining tool”, 1999. She received Dr.R Sundramoorthy award for Outstanding Academic of PSG College of Technol- ogy in the year 2006. The author has guided a project which received Best M.Tech Thesis award from Indian Society for Technical Education, New Delhi. In appreciation of publishing various technical articles the Author has received National and International Journal Publication Awards – 2000–2003. She prepared Manuals for Electronics and Instrumentation Lab and Electrical and Electronics Lab of EEE Department, PSG College of Technology, Coimbatore and also orga- nized second National Conference on Intelligent and Efﬁcient Electrical Systems in the year 2005 and conducted Short Term Courses on “Neuro Fuzzy System Princi- ples and Data Mining Applications”, November 2001 & 2003. Dr.Sumathi has pub- lished several research articles in National & International Journals/Conferences and guided many UG and PG projects. She has also published books on, “Intro- duction to Neural Networks with MATLAB”, “Introduction to Fuzzy Systems with MATLAB”, “Introduction to Data mining and its Applications” and “LabVIEW based Advanced in National / International Journals and Conferences. The Research interests include Neural Networks, Fuzzy Systems and Genetic Algorithms, Pattern Recognition and Classiﬁcation, Data Warehousing and Data Mining, Operating systems and Parallel Computing etc. Instrumentation reviewed Systems” She papers Ms.T. Hamsapriya was born at Salem on 26th September 1967. She received her B.E and M.E degree from P.S.G College of Technology, Coimbatore. She has submitted her Ph.D thesis in Parallel Computing .She is currently working as an Assistant Professor in the Department of Computer Science and Engineering, PSG College of Technology, Coimbatore. She has four years of Industrial experience and twelve years of teaching experience. She is a life member of ACS, SSI and ISTE. She is closely associated with many industrial training programmes. She has published several research papers in National and International Journals. She has reviewed papers and chaired many National/International Conferences. Her area of interests includes Parallel and Dis- tributed Computing, Grid Computing, Advanced Computer Architecture, Genetic algorithms and Neural networks. Ms. Surekha P has completed her B.E Degree in Electrical and Electronics Engi- neering in PARK College of Engineering and Technology, Coimbatore, Tamil Nadu, and Masters Degree in Control Systems at PSG College of Technology, Coimbatore, Tamil Nadu. She was a Rank Holder in both B.E and M.E Degree programme. She has received Alumni Award for best performance in curricular and co-curricular ac- tivities during her Masters Degree programme. She has presented papers in National

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