Uwe Aickelin
Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm
Aickelin, Uwe; Bull, Larry
Authors
Larry Bull
Abstract
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.
Citation
Aickelin, U., & Bull, L. Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm. Presented at Genetic and Evolutionary Computation Conference
Conference Name | Genetic and Evolutionary Computation Conference |
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Publication Date | Jan 1, 2002 |
Deposit Date | Oct 12, 2007 |
Publicly Available Date | Oct 12, 2007 |
Peer Reviewed | Peer Reviewed |
DOI | https://doi.org/10.2139/ssrn.2832053 |
Public URL | https://nottingham-repository.worktribe.com/output/1022640 |
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