Foundations of genetic algorithms 6 /:
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 a...
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| Weitere Verfasser: | , |
| Format: | Elektronisch Tagungsbericht E-Book |
| Sprache: | English |
| Veröffentlicht: |
San Francisco, Calif. :
Morgan Kaufmann,
©2001.
|
| Schriftenreihe: | Morgan Kaufmann series in evolutionary computation.
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| Schlagworte: | |
| Online-Zugang: | Volltext Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | "The 2000 Foundations of Genetic Algorithms (FOGA-6) workshop was the sixth biennial meeting in this series of workshops"--Page 1 |
| Beschreibung: | 1 online resource (342 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and indexes. |
| ISBN: | 9781558607347 155860734X 9780080506876 0080506879 |
| ISSN: | 1081-6593 |
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| 504 | |a Includes bibliographical references and indexes. | ||
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| 505 | 0 | |a Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents; Chapter 1. Introduction; Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces; Chapter 3. Niches in NK-Landscapes; Chapter 4. New Methods for Tunable, Random Landscapes; Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem; Chapter 6. Direct Statistical Estimation of GA Landscape Properties; Chapter 7. Comparing Population Mean Curves; Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment | |
| 505 | 8 | |a Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic AlgorithmsChapter 10. Towards a Theory of Strong Overgeneral Classifiers; Chapter 11. Evolutionary Optimization through PAC Learning; Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms; Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach; Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination; Chapter 15. The Mixing Rate of Different Crossover Operators; Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms | |
| 505 | 8 | |a Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and NeighborhoodsChapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index | |
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| contents | Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents; Chapter 1. Introduction; Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces; Chapter 3. Niches in NK-Landscapes; Chapter 4. New Methods for Tunable, Random Landscapes; Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem; Chapter 6. Direct Statistical Estimation of GA Landscape Properties; Chapter 7. Comparing Population Mean Curves; Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic AlgorithmsChapter 10. Towards a Theory of Strong Overgeneral Classifiers; Chapter 11. Evolutionary Optimization through PAC Learning; Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms; Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach; Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination; Chapter 15. The Mixing Rate of Different Crossover Operators; Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and NeighborhoodsChapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index |
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| discipline | Informatik |
| format | Electronic Conference Proceeding eBook |
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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.</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">"The 2000 Foundations of Genetic Algorithms (FOGA-6) workshop was the sixth biennial meeting in this series of workshops"--Page 1</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and indexes.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents; Chapter 1. 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| genre | Congress https://id.nlm.nih.gov/mesh/D016423 proceedings (reports) aat Conference papers and proceedings fast Conference papers and proceedings. lcgft http://id.loc.gov/authorities/genreForms/gf2014026068 Actes de congrès. rvmgf |
| genre_facet | Congress proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès. |
| id | ZDB-4-EBA-ocn162596067 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:16:07Z |
| institution | BVB |
| isbn | 9781558607347 155860734X 9780080506876 0080506879 |
| issn | 1081-6593 |
| language | English |
| oclc_num | 162596067 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (342 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | Morgan Kaufmann, |
| record_format | marc |
| series | Morgan Kaufmann series in evolutionary computation. |
| series2 | The Morgan Kaufmann series in evolutionary computation, |
| 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, ©2001. 1 online resource (342 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier The Morgan Kaufmann series in evolutionary computation, 1081-6593 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. "The 2000 Foundations of Genetic Algorithms (FOGA-6) workshop was the sixth biennial meeting in this series of workshops"--Page 1 Includes bibliographical references and indexes. Print version record. Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents; Chapter 1. Introduction; Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces; Chapter 3. Niches in NK-Landscapes; Chapter 4. New Methods for Tunable, Random Landscapes; Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem; Chapter 6. Direct Statistical Estimation of GA Landscape Properties; Chapter 7. Comparing Population Mean Curves; Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic AlgorithmsChapter 10. Towards a Theory of Strong Overgeneral Classifiers; Chapter 11. Evolutionary Optimization through PAC Learning; Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms; Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach; Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination; Chapter 15. The Mixing Rate of Different Crossover Operators; Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and NeighborhoodsChapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index Genetic algorithms Congresses. Algorithms Genetics Algorithmes génétiques Congrès. Algorithmes. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Genetic algorithms fast Genetische algoritmen. gtt Congress https://id.nlm.nih.gov/mesh/D016423 proceedings (reports) aat Conference papers and proceedings fast Conference papers and proceedings. lcgft http://id.loc.gov/authorities/genreForms/gf2014026068 Actes de congrès. rvmgf Martin, W. N. (Worthy N.) https://id.oclc.org/worldcat/entity/E39PCjCrY4FVJWyCCYJdfRCrVP http://id.loc.gov/authorities/names/n87939429 Spears, William M., 1962- https://id.oclc.org/worldcat/entity/E39PCjF6MFff8PJ7T8fM9dYvRC http://id.loc.gov/authorities/names/n00003617 Workshop on Foundations of Genetic Algorithms (6th : 2000 : Charlottesville, Va.) has work: Foundations of genetic algorithms 6 (Text) https://id.oclc.org/worldcat/entity/E39PCFTjywy7MDk6ydfBBqfBrm https://id.oclc.org/worldcat/ontology/hasWork Print version: Foundations of genetic algorithms 6. San Francisco, Calif. : Morgan Kaufmann, ©2001 155860734X 9781558607347 (DLC) 2001275761 (OCoLC)48163083 Online version: Foundations of genetic algorithms 6. San Francisco, Calif. : Morgan Kaufmann, ©2001 (OCoLC)795364490 Morgan Kaufmann series in evolutionary computation. 1081-6593 http://id.loc.gov/authorities/names/n2001008051 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=210504 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/book/9781558607347 Volltext |
| spellingShingle | Foundations of genetic algorithms 6 / Morgan Kaufmann series in evolutionary computation. Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents; Chapter 1. Introduction; Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces; Chapter 3. Niches in NK-Landscapes; Chapter 4. New Methods for Tunable, Random Landscapes; Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem; Chapter 6. Direct Statistical Estimation of GA Landscape Properties; Chapter 7. Comparing Population Mean Curves; Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic AlgorithmsChapter 10. Towards a Theory of Strong Overgeneral Classifiers; Chapter 11. Evolutionary Optimization through PAC Learning; Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms; Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach; Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination; Chapter 15. The Mixing Rate of Different Crossover Operators; Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and NeighborhoodsChapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index Genetic algorithms Congresses. Algorithms Genetics Algorithmes génétiques Congrès. Algorithmes. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Genetic algorithms fast Genetische algoritmen. gtt |
| subject_GND | https://id.nlm.nih.gov/mesh/D016423 http://id.loc.gov/authorities/genreForms/gf2014026068 |
| 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 | Genetic algorithms Congresses. Algorithms Genetics Algorithmes génétiques Congrès. Algorithmes. algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Genetic algorithms fast Genetische algoritmen. gtt |
| topic_facet | Genetic algorithms Congresses. Algorithms Genetics Algorithmes génétiques Congrès. Algorithmes. algorithms. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Genetic algorithms Genetische algoritmen. Congress proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=210504 https://www.sciencedirect.com/science/book/9781558607347 |
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