Genetic Programming Theory and Practice VI:
Gespeichert in:
Weitere Verfasser: | , , |
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
New York, NY
Springer US
2009
|
Ausgabe: | 1st ed. 2009 |
Schriftenreihe: | Genetic and Evolutionary Computation
|
Schlagworte: | |
Online-Zugang: | DE-355 Volltext |
Beschreibung: | 1 Online-Ressource (XIV, 274 Seiten 100 illus.) |
ISBN: | 9780387876238 |
ISSN: | 1932-0175 |
DOI: | 10.1007/978-0-387-87623-8 |
Internformat
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505 | 8 | |a 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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Datensatz im Suchindex
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contents | 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 |
ctrlnum | (ZDB-2-SCS)978-0-387-87623-8 (DE-599)BVBBV050122725 |
dewey-full | 005.11 |
dewey-hundreds | 000 - Computer science, information, general works |
dewey-ones | 005 - Computer programming, programs, data, security |
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dewey-search | 005.11 |
dewey-sort | 15.11 |
dewey-tens | 000 - Computer science, information, general works |
discipline | Informatik |
doi_str_mv | 10.1007/978-0-387-87623-8 |
edition | 1st ed. 2009 |
format | Electronic eBook |
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spelling | Genetic Programming Theory and Practice VI edited by Rick Riolo, Terence Soule, Bill Worzel 1st ed. 2009 New York, NY Springer US 2009 1 Online-Ressource (XIV, 274 Seiten 100 illus.) txt rdacontent c rdamedia cr rdacarrier Genetic and Evolutionary Computation 1932-0175 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 Programming Techniques Artificial Intelligence Theory of Computation Algorithms Computer programming Artificial intelligence Computer science Riolo, Rick edt Soule, Terence edt Worzel, Bill edt Erscheint auch als Druck-Ausgabe 9780387876221 Erscheint auch als Druck-Ausgabe 9780387876351 Erscheint auch als Druck-Ausgabe 9781441946836 https://doi.org/10.1007/978-0-387-87623-8 Verlag URL des Erstveröffentlichers Volltext |
spellingShingle | Genetic Programming Theory and Practice VI 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 Programming Techniques Artificial Intelligence Theory of Computation Algorithms Computer programming Artificial intelligence Computer science |
title | Genetic Programming Theory and Practice VI |
title_auth | Genetic Programming Theory and Practice VI |
title_exact_search | Genetic Programming Theory and Practice VI |
title_full | Genetic Programming Theory and Practice VI edited by Rick Riolo, Terence Soule, Bill Worzel |
title_fullStr | Genetic Programming Theory and Practice VI edited by Rick Riolo, Terence Soule, Bill Worzel |
title_full_unstemmed | Genetic Programming Theory and Practice VI edited by Rick Riolo, Terence Soule, Bill Worzel |
title_short | Genetic Programming Theory and Practice VI |
title_sort | genetic programming theory and practice vi |
topic | Programming Techniques Artificial Intelligence Theory of Computation Algorithms Computer programming Artificial intelligence Computer science |
topic_facet | Programming Techniques Artificial Intelligence Theory of Computation Algorithms Computer programming Artificial intelligence Computer science |
url | https://doi.org/10.1007/978-0-387-87623-8 |
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