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Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

Aickelin, Uwe; Bull, Larry

Authors

Uwe Aickelin

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.

Publication Date Jan 1, 2002
Peer Reviewed Peer Reviewed
APA6 Citation Aickelin, U., & Bull, L. (2002). Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf

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02gecco_partner.pdf (192 Kb)
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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