Adapting operator settings in genetic algorithms:
Abstract: "In the vast majority of genetic algorithm implementations, the operator settings are fixed throughout a given run. However, it has sometimes been argued that these settings should vary over the course of a genetic algorithm run -- so as to account for changes in the ability of the op...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Edinburgh
1996
|
| Schriftenreihe: | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper
821 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "In the vast majority of genetic algorithm implementations, the operator settings are fixed throughout a given run. However, it has sometimes been argued that these settings should vary over the course of a genetic algorithm run -- so as to account for changes in the ability of the operators to produce children of increased fitness. This paper describes an empirical investigation into this question. The effect upon genetic algorithm performance of two adaptation methods upon both well-studied theoretical problems, and a hard problem from Operations Research -- the flowshop sequencing problem, is examined. The results obtained indicate that the applicability of operator adaptation is problem- dependent." |
| Beschreibung: | 24 S. |
Internformat
MARC
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| 100 | 1 | |a Tuson, Andrew |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Adapting operator settings in genetic algorithms |c Tuson, A. ; Ross, P. |
| 264 | 1 | |a Edinburgh |c 1996 | |
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| 490 | 1 | |a University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |v 821 | |
| 520 | 3 | |a Abstract: "In the vast majority of genetic algorithm implementations, the operator settings are fixed throughout a given run. However, it has sometimes been argued that these settings should vary over the course of a genetic algorithm run -- so as to account for changes in the ability of the operators to produce children of increased fitness. This paper describes an empirical investigation into this question. The effect upon genetic algorithm performance of two adaptation methods upon both well-studied theoretical problems, and a hard problem from Operations Research -- the flowshop sequencing problem, is examined. The results obtained indicate that the applicability of operator adaptation is problem- dependent." | |
| 650 | 7 | |a Bionics and artificial intelligence |2 sigle | |
| 650 | 7 | |a Computer software |2 sigle | |
| 650 | 7 | |a Mathematics |2 sigle | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Genetic algorithms | |
| 650 | 4 | |a Operations research | |
| 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 821 |w (DE-604)BV010450646 |9 821 | |
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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 |
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| author_variant | a t at p r pr |
| building | Verbundindex |
| bvnumber | BV011050168 |
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| id | DE-604.BV011050168 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T18:03:11Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007400563 |
| oclc_num | 36466353 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM |
| owner_facet | DE-91G DE-BY-TUM |
| physical | 24 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 Adapting operator settings in genetic algorithms Tuson, A. ; Ross, P. Edinburgh 1996 24 S. txt rdacontent n rdamedia nc rdacarrier University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 821 Abstract: "In the vast majority of genetic algorithm implementations, the operator settings are fixed throughout a given run. However, it has sometimes been argued that these settings should vary over the course of a genetic algorithm run -- so as to account for changes in the ability of the operators to produce children of increased fitness. This paper describes an empirical investigation into this question. The effect upon genetic algorithm performance of two adaptation methods upon both well-studied theoretical problems, and a hard problem from Operations Research -- the flowshop sequencing problem, is examined. The results obtained indicate that the applicability of operator adaptation is problem- dependent." Bionics and artificial intelligence sigle Computer software sigle Mathematics sigle Mathematik Genetic algorithms Operations research Operator theory Ross, Peter Verfasser aut Department of Artificial Intelligence: DAI research paper University <Edinburgh> 821 (DE-604)BV010450646 821 |
| spellingShingle | Tuson, Andrew Ross, Peter Adapting operator settings in genetic algorithms Bionics and artificial intelligence sigle Computer software sigle Mathematics sigle Mathematik Genetic algorithms Operations research Operator theory |
| title | Adapting operator settings in genetic algorithms |
| title_auth | Adapting operator settings in genetic algorithms |
| title_exact_search | Adapting operator settings in genetic algorithms |
| title_full | Adapting operator settings in genetic algorithms Tuson, A. ; Ross, P. |
| title_fullStr | Adapting operator settings in genetic algorithms Tuson, A. ; Ross, P. |
| title_full_unstemmed | Adapting operator settings in genetic algorithms Tuson, A. ; Ross, P. |
| title_short | Adapting operator settings in genetic algorithms |
| title_sort | adapting operator settings in genetic algorithms |
| topic | Bionics and artificial intelligence sigle Computer software sigle Mathematics sigle Mathematik Genetic algorithms Operations research Operator theory |
| topic_facet | Bionics and artificial intelligence Computer software Mathematics Mathematik Genetic algorithms Operations research Operator theory |
| volume_link | (DE-604)BV010450646 |
| work_keys_str_mv | AT tusonandrew adaptingoperatorsettingsingeneticalgorithms AT rosspeter adaptingoperatorsettingsingeneticalgorithms |