Co-evolution of operator settings in genetic algorithms:
Abstract: "Typical genetic algorithm implementations use operator settings that are fixed throughout a given run. Varying these settings is known to improve performance -- the problem is knowing how to vary them. One approach is to encode the operator settings into each member of the GA populat...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Edinburgh
1996
|
| Series: | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper
789 |
| Subjects: | |
| Summary: | Abstract: "Typical genetic algorithm implementations use operator settings that are fixed throughout a given run. Varying these settings is known to improve performance -- the problem is knowing how to vary them. One approach is to encode the operator settings into each member of the GA population, and allow them to evolve. This paper describes an empirical investigation into the effect of co-evolving operator settings, for some common problems in the genetic algorithms field. The results obtained indicate that the problem representation, and the choice of operators on the encoded operator settings are important for useful adaptation." |
| Physical Description: | 8 S. |
Staff View
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| 490 | 1 | |a University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |v 789 | |
| 520 | 3 | |a Abstract: "Typical genetic algorithm implementations use operator settings that are fixed throughout a given run. Varying these settings is known to improve performance -- the problem is knowing how to vary them. One approach is to encode the operator settings into each member of the GA population, and allow them to evolve. This paper describes an empirical investigation into the effect of co-evolving operator settings, for some common problems in the genetic algorithms field. The results obtained indicate that the problem representation, and the choice of operators on the encoded operator settings are important for useful adaptation." | |
| 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 | |
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Record in the Search Index
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| id | DE-604.BV011049520 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T18:03:10Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007399976 |
| oclc_num | 35590600 |
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| physical | 8 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 Co-evolution of operator settings in genetic algorithms Tuson, A. ; Ross, P. Edinburgh 1996 8 S. txt rdacontent n rdamedia nc rdacarrier University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 789 Abstract: "Typical genetic algorithm implementations use operator settings that are fixed throughout a given run. Varying these settings is known to improve performance -- the problem is knowing how to vary them. One approach is to encode the operator settings into each member of the GA population, and allow them to evolve. This paper describes an empirical investigation into the effect of co-evolving operator settings, for some common problems in the genetic algorithms field. The results obtained indicate that the problem representation, and the choice of operators on the encoded operator settings are important for useful adaptation." 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> 789 (DE-604)BV010450646 789 |
| spellingShingle | Tuson, Andrew Ross, Peter Co-evolution of operator settings in genetic algorithms Applied statistics, operational research sigle Bionics and artificial intelligence sigle Evolutionary computation Genetic algorithms Operator theory |
| title | Co-evolution of operator settings in genetic algorithms |
| title_auth | Co-evolution of operator settings in genetic algorithms |
| title_exact_search | Co-evolution of operator settings in genetic algorithms |
| title_full | Co-evolution of operator settings in genetic algorithms Tuson, A. ; Ross, P. |
| title_fullStr | Co-evolution of operator settings in genetic algorithms Tuson, A. ; Ross, P. |
| title_full_unstemmed | Co-evolution of operator settings in genetic algorithms Tuson, A. ; Ross, P. |
| title_short | Co-evolution of operator settings in genetic algorithms |
| title_sort | co evolution of operator settings in genetic algorithms |
| 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 coevolutionofoperatorsettingsingeneticalgorithms AT rosspeter coevolutionofoperatorsettingsingeneticalgorithms |