An Introduction to Genetic Algorithms (Paperback)

Description

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.

 

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