Enhanced descriptions from Syndetics:

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics-particularly in machine learning, scientific modeling, and artificial life-and reviews a broad span of research, including the work of Mitchell and her colleagues.

The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.

An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

Table of contents provided by Syndetics

• Preface
• Acknowledgments
• Genetic Algorithms: An Overview
• 1.1 A Brief History of Evolutionary Computation
• 1.2 The Appeal of Evolution
• 1.3 Biological Terminology
• 1.4 Search Spaces and Fitness Landscapes
• 1.5 Elements Of Genetic Algorithms
• 1.6 A Simple Genetic Algorithm
• 1.7 Genetic Algorithms and Traditional Search Methods
• 1.8 Some Applications of Genetic Algorithms
• 1.9 Two Brief Examples
• 1.10 How Do Genetic Algorithms Work?
• Genetic Algorithms in Problem Solving
• 2.1 Evolving Computer Programs
• 2.2 Data Analysis and Prediction
• 2.3 Evolving Neural Networks
• Genetic Algorithms in Scientific Models
• 3.1 Modeling Interactions Between Learning And Evolution
• 3.2 Modeling Sexual Selection
• 3.3 Modeling Ecosystems
• 3.4 Measuring Evolutionary Activity
• Theoretical Foundations of Genetic Algorithms
• 4.1 Schemas and the Two-Armed Bandit Problem
• 4.2 Royal Roads
• 4.3 Exact Mathematical Models Of Simple Genetic Algorithms
• 4.4 Statistical-Mechanics Approaches
• Implementing a Genetic Algorithm
• 5.1 When Should a Genetic Algorithm Be Used?
• 5.2 Encoding a Problem for a Genetic Algorithm
• 5.3 Adapting the Encoding
• 5.4 Selection Methods
• 5.5 Genetic Operators
• 5.6 Parameters for Genetic Algorithms
• Conclusions and Future Directions
• Incorporating Ecological Interactions
• Incorporating New Ideas from Genetics
• Incorporating Development and Learning
• Adapting Encodings and Using Encodings That Permit Hierarchy and Open-Endedness
• Adapting Parameters
• Connections with the Mathematical Genetics Literature
• Extension of Statistical Mechanics Approaches
• Identifying and Overcoming Impediments to the Success of GAs
• Understanding the Role of Schemas in GAs
• Understanding the Role of Crossover
• Theory of GAs With Endogenous Fitness
• Appendix A Selected General References
• Appendix B Other Resources
• Selected Journals Publishing Work on Genetic Algorithms
• Selected Annual or Biannual Conferences Including Work on Genetic Algorithms
• Internet Mailing Lists, World Wide Web Sites, and News Groups with Information and Discussions on Ge...
• Bibliography
• Index

Reviews provided by Syndetics

CHOICE Review

Mitchell provides a delightful and interesting introduction to a very fascinating and fast-growing field of artificial intelligence. Genetic algorithms are based on the principles of evolution and genetics. Although these principles are less evident in biology, their engineering applications have been shown in recent years to produce remarkable results in solving many difficult problems. The author has done a superb job in conveying the basic concepts and techniques of genetic algorithms without overwhelming the reader with formulas and equations. The goal is to let the reader gain some practical knowledge and understand the limitations of this technology through simple examples. At the end of each chapter, thought exercises serve to reinforce the ideas presented, and computer exercises help the reader to gain practical appreciation. For those more rigorously inclined, an extensive bibliography at the end is a good starting point for further study. Written for scientists in all disciplines; thus, only college-level mathematics is needed. General; lower-division undergraduates; faculty; professionals; two-year technical program students. J. Y. Cheung University of Oklahoma

Author notes provided by Syndetics

Melanie Mitchell, Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, is a Fellow of the Michigan Society of Fellows. She is also Director of the Adaptive Computation Program at the Santa Fe Institute.