Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm
Neural and Evolutionary Computing
2010-07-05 v2 Artificial Intelligence
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.
Cite
@article{arxiv.0801.3550,
title = {Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm},
author = {Uwe Aickelin and Larry Bull},
journal= {arXiv preprint arXiv:0801.3550},
year = {2010}
}