Genetic Programming Theory and Practice VI [electronic resource] /edited by Bill Worzel, Terence Soule, Rick Riolo.
by Worzel, Bill [editor.]; Soule, Terence [editor.]; Riolo, Rick [editor.]; SpringerLink (Online service).
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BookSeries: Genetic and Evolutionary Computation: Publisher: Boston, MA : Springer US, 2009.Description: online resource.ISBN: 9780387876238.Subject(s): Computer science | Information theory | Computer software | Electronic data processing | Artificial intelligence | Computer Science | Programming Techniques | Algorithm Analysis and Problem Complexity | Theory of Computation | Artificial Intelligence (incl. Robotics) | Computing MethodologiesOnline resources: Click here to access online
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Springer eBooksSummary: Genetic Programming Theory and Practice VI was developed from the sixth workshop at the University of Michigan's Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. These contributions address several significant inter-dependent themes which emerged from this year's workshop, including: Making efficient and effective use of test data Sustaining the long term evolvability of our GP systems Exploiting discovered subsolutions for reuse Increasing the role of a Domain Expert In the course of investigating these themes, the chapters describe a variety of techniques in widespread use among practitioners who deal with industrial-scale, real-world problems, such as: Pareto optimization, particularly as a means to limit solution complexity Various types of age-layered populations or niching mechanisms Data partitioning, a priori or adaptively, e.g., via co-evolution Cluster computing or general purpose graphics processors for parallel computing Ensemble/team solutions This work covers applications of GP to a host of domains, including bioinformatics, symbolic regression for system modeling in various settings, circuit design, and financial modeling to support portfolio management. This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.
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Genetic Programming Theory and Practice VI was developed from the sixth workshop at the University of Michigan's Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. These contributions address several significant inter-dependent themes which emerged from this year's workshop, including: Making efficient and effective use of test data Sustaining the long term evolvability of our GP systems Exploiting discovered subsolutions for reuse Increasing the role of a Domain Expert In the course of investigating these themes, the chapters describe a variety of techniques in widespread use among practitioners who deal with industrial-scale, real-world problems, such as: Pareto optimization, particularly as a means to limit solution complexity Various types of age-layered populations or niching mechanisms Data partitioning, a priori or adaptively, e.g., via co-evolution Cluster computing or general purpose graphics processors for parallel computing Ensemble/team solutions This work covers applications of GP to a host of domains, including bioinformatics, symbolic regression for system modeling in various settings, circuit design, and financial modeling to support portfolio management. This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.
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