Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm
This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2002
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| Online Access: | https://eprints.nottingham.ac.uk/633/ |
| _version_ | 1848790449910710272 |
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| author | Aickelin, Uwe Bull, Larry |
| author_facet | Aickelin, Uwe Bull, Larry |
| author_sort | Aickelin, Uwe |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes for (sub-)fitness evaluation purposes are examined for two multiple-choice optimisation problems. It is shown that random partnering strategies perform best by providing better sampling and more diversity. |
| first_indexed | 2025-11-14T18:12:48Z |
| format | Conference or Workshop Item |
| id | nottingham-633 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:12:48Z |
| publishDate | 2002 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-6332020-05-04T20:32:14Z https://eprints.nottingham.ac.uk/633/ Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm Aickelin, Uwe Bull, Larry This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes for (sub-)fitness evaluation purposes are examined for two multiple-choice optimisation problems. It is shown that random partnering strategies perform best by providing better sampling and more diversity. 2002 Conference or Workshop Item PeerReviewed Aickelin, Uwe and Bull, Larry (2002) Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm. In: Genetic and Evolutionary Computation Conference, 2002, New York, USA. |
| spellingShingle | Aickelin, Uwe Bull, Larry Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm |
| title | Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm |
| title_full | Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm |
| title_fullStr | Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm |
| title_full_unstemmed | Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm |
| title_short | Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm |
| title_sort | partnering strategies for fitness evaluation in a pyramidal evolutionary algorithm |
| url | https://eprints.nottingham.ac.uk/633/ |