Foundations of genetic algorithms 6:
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
San Francisco, Calif.
Morgan Kaufmann
c2001
|
| Schriftenreihe: | Morgan Kaufmann series in evolutionary computation
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | "The 2000 Foundations of Genetic Algorithms (FOGA-6) workshop was the sixth biennial meeting in this series of workshops"--P. 1 Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems. Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones. Includes research from academia, government laboratories, and industry Contains high calibre papers which have been extensively reviewed Continues the tradition of presenting not only current theoretical work but also issues that could shape future research in the field Ideal for researchers in machine learning, specifically those involved with evolutionary computation Includes bibliographical references and indexes Machine generated contents note: Introduction-- Worthy N. Martin and William M. Spears -- Overcoming Fitness Barriers in Multi-Modal Search Spaces5 -- Martin J. Oates and David Come -- N iches in N K -Landscapes27 -- Keith E. Mathia, Larry J. Eshelman, and J. David Schaffer -- New Methods for Tunable, Random Landscapes 47 -- R.E. Smith and J.E. Smith -- Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem69 -- Richard A. Watson -- Direct Statistical Estimation of GA Landscape Properties 91 -- Colin R. Reeves -- Comparing Population Mean Curves109 -- B. Naudts and I. Landrieu -- Local Performance of the ((/(I, () -ES in a Noisy Environment 127 -- Dirk V Arnold and Hans-Georg Beyer -- Recursive Conditional Scheme Theorem, Convergence and -- Population Sizing in Genetic Algorithms 143 -- Riccardo Poli -- Towards a Theory of Strong Overgeneral Classifiers 165 -- Tim Kovacs -- Evolutionary Optimization through PAC Learning 185 -- Forbes J. Burkowski -- Continuous Dynamical System Models of Steady-State Genetic Algorithms209 -- Alden H. Wright and Jonathan E. Rowe -- Mutation-Selection Algorithm: A Large Deviation Approach 227 -- Paul Albuquerque and Christian Mazza -- The Equilibrium and Transient Behavior of Mutation and Recombination 241 -- William M. Spears -- The Mixing Rate of Different Crossover Operators 261 -- Adam Prigel-Bennett -- Dynamic Parameter Control in Simple Evolutionary Algorithms 275 -- Stefan Droste, Thomas Jansen, and Ingo Wegener -- Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods295 -- Darrell Whitley, Laura Barbulescu, and Jean-Paul Watson -- Burden and Benefits of Redundancy 313 -- Karsten Weicker and Nicole Weicker -- Author Index 335 -- Key Word Index337 |
| Beschreibung: | 1 Online-Ressource (342 p.) |
| ISBN: | 9781558607347 155860734X 9780080506876 0080506879 |
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| 500 | |a Includes bibliographical references and indexes | ||
| 500 | |a Machine generated contents note: Introduction-- Worthy N. Martin and William M. Spears -- Overcoming Fitness Barriers in Multi-Modal Search Spaces5 -- Martin J. Oates and David Come -- N iches in N K -Landscapes27 -- Keith E. Mathia, Larry J. Eshelman, and J. David Schaffer -- New Methods for Tunable, Random Landscapes 47 -- R.E. Smith and J.E. Smith -- Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem69 -- Richard A. Watson -- Direct Statistical Estimation of GA Landscape Properties 91 -- Colin R. Reeves -- Comparing Population Mean Curves109 -- B. Naudts and I. Landrieu -- Local Performance of the ((/(I, () -ES in a Noisy Environment 127 -- Dirk V Arnold and Hans-Georg Beyer -- Recursive Conditional Scheme Theorem, Convergence and -- Population Sizing in Genetic Algorithms 143 -- Riccardo Poli -- Towards a Theory of Strong Overgeneral Classifiers 165 -- Tim Kovacs -- Evolutionary Optimization through PAC Learning 185 -- Forbes J. Burkowski -- Continuous Dynamical System Models of Steady-State Genetic Algorithms209 -- Alden H. Wright and Jonathan E. Rowe -- Mutation-Selection Algorithm: A Large Deviation Approach 227 -- Paul Albuquerque and Christian Mazza -- The Equilibrium and Transient Behavior of Mutation and Recombination 241 -- William M. Spears -- The Mixing Rate of Different Crossover Operators 261 -- Adam Prigel-Bennett -- Dynamic Parameter Control in Simple Evolutionary Algorithms 275 -- Stefan Droste, Thomas Jansen, and Ingo Wegener -- Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods295 -- Darrell Whitley, Laura Barbulescu, and Jean-Paul Watson -- Burden and Benefits of Redundancy 313 -- Karsten Weicker and Nicole Weicker -- Author Index 335 -- Key Word Index337 | ||
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| id | DE-604.BV042957096 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:13:42Z |
| institution | BVB |
| isbn | 9781558607347 155860734X 9780080506876 0080506879 |
| language | English |
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| spelling | Foundations of genetic algorithms 6 edited by Worthy N. Martin and William M. Spears Foundations of genetic algorithms six San Francisco, Calif. Morgan Kaufmann c2001 1 Online-Ressource (342 p.) txt rdacontent c rdamedia cr rdacarrier Morgan Kaufmann series in evolutionary computation "The 2000 Foundations of Genetic Algorithms (FOGA-6) workshop was the sixth biennial meeting in this series of workshops"--P. 1 Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems. Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones. Includes research from academia, government laboratories, and industry Contains high calibre papers which have been extensively reviewed Continues the tradition of presenting not only current theoretical work but also issues that could shape future research in the field Ideal for researchers in machine learning, specifically those involved with evolutionary computation Includes bibliographical references and indexes Machine generated contents note: Introduction-- Worthy N. Martin and William M. Spears -- Overcoming Fitness Barriers in Multi-Modal Search Spaces5 -- Martin J. Oates and David Come -- N iches in N K -Landscapes27 -- Keith E. Mathia, Larry J. Eshelman, and J. David Schaffer -- New Methods for Tunable, Random Landscapes 47 -- R.E. Smith and J.E. Smith -- Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem69 -- Richard A. Watson -- Direct Statistical Estimation of GA Landscape Properties 91 -- Colin R. Reeves -- Comparing Population Mean Curves109 -- B. Naudts and I. Landrieu -- Local Performance of the ((/(I, () -ES in a Noisy Environment 127 -- Dirk V Arnold and Hans-Georg Beyer -- Recursive Conditional Scheme Theorem, Convergence and -- Population Sizing in Genetic Algorithms 143 -- Riccardo Poli -- Towards a Theory of Strong Overgeneral Classifiers 165 -- Tim Kovacs -- Evolutionary Optimization through PAC Learning 185 -- Forbes J. Burkowski -- Continuous Dynamical System Models of Steady-State Genetic Algorithms209 -- Alden H. Wright and Jonathan E. Rowe -- Mutation-Selection Algorithm: A Large Deviation Approach 227 -- Paul Albuquerque and Christian Mazza -- The Equilibrium and Transient Behavior of Mutation and Recombination 241 -- William M. Spears -- The Mixing Rate of Different Crossover Operators 261 -- Adam Prigel-Bennett -- Dynamic Parameter Control in Simple Evolutionary Algorithms 275 -- Stefan Droste, Thomas Jansen, and Ingo Wegener -- Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods295 -- Darrell Whitley, Laura Barbulescu, and Jean-Paul Watson -- Burden and Benefits of Redundancy 313 -- Karsten Weicker and Nicole Weicker -- Author Index 335 -- Key Word Index337 Genetische algoritmen gtt COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Genetic algorithms fast Genetic algorithms Congresses (DE-588)1071861417 Konferenzschrift gnd-content Martin, W. N. Sonstige oth Spears, William M. Sonstige oth Workshop on Foundations of Genetic Algorithms < 2000, Charlottesville, Va.> Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=210504 Aggregator Volltext |
| spellingShingle | Foundations of genetic algorithms 6 Genetische algoritmen gtt COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Genetic algorithms fast Genetic algorithms Congresses |
| subject_GND | (DE-588)1071861417 |
| title | Foundations of genetic algorithms 6 |
| title_alt | Foundations of genetic algorithms six |
| title_auth | Foundations of genetic algorithms 6 |
| title_exact_search | Foundations of genetic algorithms 6 |
| title_full | Foundations of genetic algorithms 6 edited by Worthy N. Martin and William M. Spears |
| title_fullStr | Foundations of genetic algorithms 6 edited by Worthy N. Martin and William M. Spears |
| title_full_unstemmed | Foundations of genetic algorithms 6 edited by Worthy N. Martin and William M. Spears |
| title_short | Foundations of genetic algorithms 6 |
| title_sort | foundations of genetic algorithms 6 |
| topic | Genetische algoritmen gtt COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Genetic algorithms fast Genetic algorithms Congresses |
| topic_facet | Genetische algoritmen COMPUTERS / Enterprise Applications / Business Intelligence Tools COMPUTERS / Intelligence (AI) & Semantics Genetic algorithms Genetic algorithms Congresses Konferenzschrift |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=210504 |
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