A Pyramidal Genetic Algorithm for Multiple-Choice Problems
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 Author: | |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2001
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| Online Access: | https://eprints.nottingham.ac.uk/254/ |
| _version_ | 1848790380082888704 |
|---|---|
| author | Aickelin, Uwe |
| author_facet | Aickelin, Uwe |
| 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 amongst the agents on solution quality are examined for two multiple-choice optimisation problems. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements. |
| first_indexed | 2025-11-14T18:11:41Z |
| format | Conference or Workshop Item |
| id | nottingham-254 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:11:41Z |
| publishDate | 2001 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-2542021-05-31T14:47:41Z https://eprints.nottingham.ac.uk/254/ A Pyramidal Genetic Algorithm for Multiple-Choice Problems Aickelin, Uwe 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 amongst the agents on solution quality are examined for two multiple-choice optimisation problems. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements. 2001 Conference or Workshop Item PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/254/1/01or43_partner.pdf Aickelin, Uwe (2001) A Pyramidal Genetic Algorithm for Multiple-Choice Problems. In: Annual Operational Research Conference 43, Bath. |
| spellingShingle | Aickelin, Uwe A Pyramidal Genetic Algorithm for Multiple-Choice Problems |
| title | A Pyramidal Genetic Algorithm for Multiple-Choice Problems |
| title_full | A Pyramidal Genetic Algorithm for Multiple-Choice Problems |
| title_fullStr | A Pyramidal Genetic Algorithm for Multiple-Choice Problems |
| title_full_unstemmed | A Pyramidal Genetic Algorithm for Multiple-Choice Problems |
| title_short | A Pyramidal Genetic Algorithm for Multiple-Choice Problems |
| title_sort | pyramidal genetic algorithm for multiple-choice problems |
| url | https://eprints.nottingham.ac.uk/254/ |