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 |
| Language: | English |
| Published: |
2002
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| Online Access: | https://eprints.nottingham.ac.uk/255/ |
| _version_ | 1848790380368101376 |
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| author | Aickelin, Uwe Bull, L |
| author_facet | Aickelin, Uwe Bull, L |
| 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 | 2025-11-14T18:11:42Z |
| format | Conference or Workshop Item |
| id | nottingham-255 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:11:42Z |
| publishDate | 2002 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-2552021-05-31T14:47:42Z https://eprints.nottingham.ac.uk/255/ Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm Aickelin, Uwe Bull, L 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 application/pdf en https://eprints.nottingham.ac.uk/255/1/02gecco_partner.pdf Aickelin, Uwe and Bull, L (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, L 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/255/ |