A Pyramidal Genetic Algorithm for Multiple-Choice Problems

Aickelin, Uwe

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.

• 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: https://nottingham-repository.worktribe.com/output/1023143