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Genetic Programming Theory and Practice III explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). This contributed volume was developed from the third workshop at the University of Michigan’s Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to this rapidly advancing field. The text provides a cohesive view of the issues facing both practitioners and theoreticians and examines the synergy between GP theory and application.
The foremost international researchers and practitioners in the GP arena contributed to the volume, discussing such topics as:
techniques to enhance GP capabilities with real-world applications and real-world application success stories from a variety of domains, including chemical and process control, informatics, and circuit design
visualization models to understand GP processing and
open challenges facing the community and potential research directions
Genetic Programming Theory and Practice III provides the most recent developments in GP theory, practice, and the integration of theory and practice. This text, the result of an extensive dialog between GP theoreticians and practitioners, is a unique and indispensable tool for both academics and industry professionals interested in the GP realm.
Contributing Authors.- Preface.- Foreword.- Genetic Programming: Theory and Practice.- Evolving Swarming Agents in Real Time.- Automated Design of a Previously Patented Aspherical Optical Lens System by Means of Genetic Programming.- Discrimination of Unexploded Ordnance from Clutter Using Linear Genetic Programming.- Rapid Re-evolution of an X-Band Antenna for NASA’s Space Technology 5 Mission.- Variable Selection in Industrial Datasets Using Pareto Genetic Programming.- A Higher-Order Function Approach to Evolve Recursive Programs.- Trivial Geography in Genetic Programming.- Running Genetic Programming Backwards.- An Examination of Simultaneous Evolution of Grammars and Solutions.- The Importance of Local Search.- Content Diversity in Genetic Programming and its Correlation with Fitness.- Genetic Programming Inside a Cell.- Evolution on Neutral Networks in Genetic Programming.- The Effects of Size and Depth Limits on Tree Based Genetic Programming.- Application Issues of Genetic Programming in Industry.- Challenges in Open-Ended Problem Solving with Genetic Programming.- Domain Specificity of Genetic Programming Based Automated Synthesis: A Case Study with Synthesis of Mechanical Vibration Absorbers.- Genetic Programming in Industrial Analog CAD: Applications and Challenges.- Index.