Memetic Networks: Analyzing the Effects of Network Properties in Multi-Agent Performance
Abstract
We explore the relationship between properties of the network defined by connected agents and the global sys-tem performance. This is achieved by means of a novel class of optimization algorithms. This new class makes explicit use of an underlying network that structures the information flow between multiple agents performing local searches. We show that this new class of algo-rithms is competitive with respect to other population-based optimization techniques. Finally, we demonstrate by numerical simulations that changes in the way the network is built leads to variations in the system’s per-formance. In particular, we show how constrained hubs- highly connected agents- can be beneficial in particu-lar optimization problems.
Cite
Text
Araújo and Lamb. "Memetic Networks: Analyzing the Effects of Network Properties in Multi-Agent Performance." AAAI Conference on Artificial Intelligence, 2008.Markdown
[Araújo and Lamb. "Memetic Networks: Analyzing the Effects of Network Properties in Multi-Agent Performance." AAAI Conference on Artificial Intelligence, 2008.](https://mlanthology.org/aaai/2008/araujo2008aaai-memetic/)BibTeX
@inproceedings{araujo2008aaai-memetic,
title = {{Memetic Networks: Analyzing the Effects of Network Properties in Multi-Agent Performance}},
author = {Araújo, Ricardo M. and Lamb, Luís C.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2008},
pages = {3-8},
url = {https://mlanthology.org/aaai/2008/araujo2008aaai-memetic/}
}