Self-adaptation by co-evolution:
Abstract: "Traditional genetic algorithms use operator settings such as the crossover rate or number of crossover points that are fixed throughout a given run. The choice of settings can have a major effect on performance, but finding good settings can be hard. One option is to encode the opera...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Edinburgh
1996
|
| Schriftenreihe: | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper
788 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "Traditional genetic algorithms use operator settings such as the crossover rate or number of crossover points that are fixed throughout a given run. The choice of settings can have a major effect on performance, but finding good settings can be hard. One option is to encode the operator settings onto each member of the GA population, and allow them to evolve too. This paper describes an empirical investigation into co-evolving operator settings in genetic algorithms. The results indicate that the problem representation and the choice of operators that are applied to the encoded operator settings is important for useful adaptation to take place." |
| Beschreibung: | 6 S. |
Internformat
MARC
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| 100 | 1 | |a Tuson, Andrew |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Self-adaptation by co-evolution |c Tuson, A. ; Ross, P. |
| 264 | 1 | |a Edinburgh |c 1996 | |
| 300 | |a 6 S. | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 1 | |a University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |v 788 | |
| 520 | 3 | |a Abstract: "Traditional genetic algorithms use operator settings such as the crossover rate or number of crossover points that are fixed throughout a given run. The choice of settings can have a major effect on performance, but finding good settings can be hard. One option is to encode the operator settings onto each member of the GA population, and allow them to evolve too. This paper describes an empirical investigation into co-evolving operator settings in genetic algorithms. The results indicate that the problem representation and the choice of operators that are applied to the encoded operator settings is important for useful adaptation to take place." | |
| 650 | 7 | |a Applied statistics, operational research |2 sigle | |
| 650 | 7 | |a Bionics and artificial intelligence |2 sigle | |
| 650 | 4 | |a Evolutionary computation | |
| 650 | 4 | |a Genetic algorithms | |
| 650 | 4 | |a Operator theory | |
| 700 | 1 | |a Ross, Peter |e Verfasser |4 aut | |
| 810 | 2 | |a Department of Artificial Intelligence: DAI research paper |t University <Edinburgh> |v 788 |w (DE-604)BV010450646 |9 788 | |
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Datensatz im Suchindex
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| author | Tuson, Andrew Ross, Peter |
| author_facet | Tuson, Andrew Ross, Peter |
| author_role | aut aut |
| author_sort | Tuson, Andrew |
| author_variant | a t at p r pr |
| building | Verbundindex |
| bvnumber | BV011049508 |
| ctrlnum | (OCoLC)35590622 (DE-599)BVBBV011049508 |
| format | Book |
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| id | DE-604.BV011049508 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T18:03:10Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007399966 |
| oclc_num | 35590622 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM |
| owner_facet | DE-91G DE-BY-TUM |
| physical | 6 S. |
| publishDate | 1996 |
| publishDateSearch | 1996 |
| publishDateSort | 1996 |
| record_format | marc |
| series2 | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |
| spelling | Tuson, Andrew Verfasser aut Self-adaptation by co-evolution Tuson, A. ; Ross, P. Edinburgh 1996 6 S. txt rdacontent n rdamedia nc rdacarrier University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 788 Abstract: "Traditional genetic algorithms use operator settings such as the crossover rate or number of crossover points that are fixed throughout a given run. The choice of settings can have a major effect on performance, but finding good settings can be hard. One option is to encode the operator settings onto each member of the GA population, and allow them to evolve too. This paper describes an empirical investigation into co-evolving operator settings in genetic algorithms. The results indicate that the problem representation and the choice of operators that are applied to the encoded operator settings is important for useful adaptation to take place." Applied statistics, operational research sigle Bionics and artificial intelligence sigle Evolutionary computation Genetic algorithms Operator theory Ross, Peter Verfasser aut Department of Artificial Intelligence: DAI research paper University <Edinburgh> 788 (DE-604)BV010450646 788 |
| spellingShingle | Tuson, Andrew Ross, Peter Self-adaptation by co-evolution Applied statistics, operational research sigle Bionics and artificial intelligence sigle Evolutionary computation Genetic algorithms Operator theory |
| title | Self-adaptation by co-evolution |
| title_auth | Self-adaptation by co-evolution |
| title_exact_search | Self-adaptation by co-evolution |
| title_full | Self-adaptation by co-evolution Tuson, A. ; Ross, P. |
| title_fullStr | Self-adaptation by co-evolution Tuson, A. ; Ross, P. |
| title_full_unstemmed | Self-adaptation by co-evolution Tuson, A. ; Ross, P. |
| title_short | Self-adaptation by co-evolution |
| title_sort | self adaptation by co evolution |
| topic | Applied statistics, operational research sigle Bionics and artificial intelligence sigle Evolutionary computation Genetic algorithms Operator theory |
| topic_facet | Applied statistics, operational research Bionics and artificial intelligence Evolutionary computation Genetic algorithms Operator theory |
| volume_link | (DE-604)BV010450646 |
| work_keys_str_mv | AT tusonandrew selfadaptationbycoevolution AT rosspeter selfadaptationbycoevolution |