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<META name=vsisbn content="0849398010">
<META name=vstitle content="Industrial Applications of Genetic Algorithms">
<META name=vsauthor content="Charles Karr; L. Michael Freeman">
<META name=vsimprint content="CRC Press">
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<META name=vspubdate content="12/01/98">
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<TITLE>Industrial Applications of Genetic Algorithms:Genetic Algorithms in the Engineer's Toolbox</TITLE>
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<P>It is important to note a special characteristic of the GA course offered at the University of Alabama: the course is offered to numerous off-campus students. The University of Alabama has been a leader in distance learning, developing numerous technology-enhanced classrooms that are used to teach courses which are video-taped and made available to students through a program called Quality University Extended Site Telecourses (QUEST). This program allows students, often working professionals interested in obtaining graduate degrees, exposure to the same lectures offered to students on site. In addition, the GA course is made available to students at the National Technological University (NTU) who are nearly always working professionals. The inclusion of off-campus students is important to note because many of the students take the course with the goal of using a GA to solve a specific search or optimization problem; many of the projects represent real-world problems of interest to industry.
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<P>Despite the particulars of the course offered at the University of Alabama, it is not dissimilar to GA courses at numerous universities.</P>
<P><FONT SIZE="+1"><B>ABOUT THIS BOOK</B></FONT></P>
<P>The editors’ motivation in producing this book is to demonstrate the applicability of GAs by users who have had a minimum exposure to the technique. Most of the students have a limited knowledge of GAs, if any, when they begin the course. Since they formulate the solutions to their applications after the first six weeks of the course, they provide a benchmark for what a user can achieve using a GA after a moderate introduction.
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<P>The chapters that comprise this book are extended versions of the students’ semester projects completed during the 1996 Fall semester. There are three exceptions: (1) Chapter 12 written by Barry Weck represents a project completed as a GA class project in a prior semester, (2) the work described in Chapter 16 by Brown Cribbs was not done in conjunction with the GA course, but was done as a graduate project while Mr. Cribbs was a student at the University of Alabama, and (3) the work described in Chapter 8 by Angie Reichert was completed as a special problems course while Ms. Reichert was an undergraduate student. Although it is true that many of the chapters include extensions to the semester projects, the majority of the work presented was completed during the semester. Much of the additional effort put forth by the students was spent in rewriting their reports so that they are in a consistent format.</P>
<P>The applications presented cover a rich and diverse array of topics. This is due in part to the fact that the students themselves have very different backgrounds. It is, however, also due to the fact that the students develop an interest in the different capabilities provided by GAs. Most of the chapters in this book focus on the use of GAs for solving engineering optimization problems. There are, however, also chapters that address the use of learning classifier systems and genetic programming. No matter what the particulars of the problem discussed, the intent of this book is to provide examples of imaginative coding schemes and effective fitness functions in hopes that they will encourage readers to develop their own fitness functions and coding schemes, and to demonstrate that GAs should now fit easily into the practicing engineer’s toolbox.</P>
<P><FONT SIZE="+1"><B>SUMMARY</B></FONT></P>
<P>GAs have come a long way since their inception by John Holland at the University of Michigan in the early 1970s. Their history is rich with insightful discoveries by a variety of researchers including many of Holland’s students. These search techniques based on natural genetics are no longer just abstract concepts suitable for solving academic problems; they are effective optimization and machine learning tools that can be used by practicing engineers and scientists to solve complex, real-world problems.
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<P>At one point, successful GA applications required (and were often driven by) a GA expert who brought to the problem a detailed working knowledge of GA theory. Although a familiarity of how a GA operates is certainly valuable in complex problems, such knowledge is not required to use a GA effectively in most applications. Rather, what is required is a detailed knowledge of the problem domain, access to efficient GA code, and an understanding of how to formulate the problem so that a GA can be employed in its solution. In other words, GAs have advanced to the point where professionals who are comfortable writing computer code can apply a GA to their particular problems of interest; GAs have made their way into the practicing engineer’s toolbox.</P>
<P>The goal of this chapter is to lay the foundation for the remainder of the book. Subsequent chapters describe computer projects completed by graduate students in a semester course on GAs offered at the University of Alabama. The complexity of the problems that are presented, and the wide spectrum of application domains, serve to provide readers with an indication of the robust nature of GAs. Additionally, the coding schemes and fitness functions described should provide the reader with examples from which they can draw in their efforts to implement GAs in their own problems.</P><P><BR></P>
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