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A Pyramidal Genetic Algorithm for Multiple-Choice Problems

Aickelin, Uwe

Authors

Uwe Aickelin



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 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.

Citation

Aickelin, U. (2001). A Pyramidal Genetic Algorithm for Multiple-Choice Problems

Conference Name Annual Operational Research Conference 43
Publication Date Jan 1, 2001
Deposit Date Oct 12, 2007
Publicly Available Date Oct 12, 2007
Peer Reviewed Peer Reviewed
Public URL http://eprints.nottingham.ac.uk/id/eprint/639
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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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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