The robust capability of evolutionary algorithms (EAs) to find solutions to difficult problems has permitted them to become popular as optimization and search techniques for many industries. Despite the success of EAs, the resultant solutions are often fragile and prone to failure when the problem changes, usually requiring human intervention to keep the EA on track. Since many optimization problems in engineering, finance, and information technology require systems that can adapt to changes over time, it is desirable that EAs be able to respond to changes in the environment on their own. This book provides an analysis of what an EA needs to do to automatically and continuously solve dynamic problems, focusing on detecting changes in the problem environment and responding to those changes. In this book we identify and quantify a key attribute needed to improve the detection and response performance of EAs in dynamic environments. We then create an enhanced EA, designed explicitly to exploit this new understanding. This enhanced EA is shown to have superior performance on some types of problems. Our experiments evaluating this enhanced EA indicate some pre viously unknown relationships between performance and diversity that may lead to general methods for improving EAs in dynamic environments. Along the way, several other important design issues are addressed involving com putational efficiency, performance measurement, and the testing of EAs in dynamic environments. Preface......Page 3 Contents......Page 4 Overview and Background......Page 8 Previous Research......Page 13 Open Research Issues......Page 16 Book Structure......Page 18 Non-stationary Problems......Page 20 Algorithm Attributes......Page 22 Biological Systems......Page 25 Engineering Control Systems......Page 27 Summary......Page 29 Efficient Diversity Measurement......Page 30 Improved Diversity Measurement for Dynamic Problems......Page 47 Summary......Page 57 New EA Design Goals......Page 58 Sentinel Placement......Page 62 Summary......Page 73 Problem Generator Background......Page 74 Generator Requirements......Page 75 Problem Generator Description and Features......Page 77 Test Problem Description......Page 83 Summary......Page 89 Issues in Performance Measurement......Page 90 Performance Measurement: Collective Mean Fitness......Page 92 Summary......Page 97 Introduction......Page 98 Overview of Comparison to Other Techniques......Page 106 Comparison Analysis and Combined Techniques......Page 109 Relationship Between Collective Fitness and Collective Dispersion......Page 117 Important Dispersion Levels for Different Movement Periods......Page 125 Background......Page 128 Experiment......Page 129 Experimental Results......Page 130 Research Results......Page 137 Open Issues and Suggested Areas for Future Research......Page 139 Conclusion......Page 141 Notation......Page 142 Refs......Page 144 Index......Page 149 The robust capability of Evolutionary Algorithms (EAs) to find solutions to difficult problems has permitted them to become the optimization and search techniques of choice for many practical static problems. Despite this success in many different environments, EAs are often prone to failure when subjected to even small changes in the problem. Effective solutions for many real-world engineering and economic problems require systems that adapt to changes over time. This book addresses the issues involved in the design of EAs that successfully operate in dynamic environments without human intervention, and provides a method for creating EAs for these environments
