Swarm Behaviour Evolution via Rule Sharing and Novelty Search
Abstract
We present in this paper an exertion of our previous work by increasing the robustness and coverage of the evolution search via hybridisation with a state-of-the-art novelty search and accelerate the individual agent behaviour searches via a novel behaviour-component sharing technique. Via these improvements, we present Swarm Learning Classifier System 2.0 (SLCS2), a behaviour evolving algorithm which is robust to complex environments, and seen to out-perform a human behaviour designer in challenging cases of the data-transfer task in a range of environmental conditions. Additionally, we examine the impact of tailoring the SLCS2 rule generator for specific environmental conditions. We find this leads to over-fitting, as might be expected, and thus conclude that for greatest environment flexibility a general rule generator should be utilised.
Cite
@article{arxiv.1910.12412,
title = {Swarm Behaviour Evolution via Rule Sharing and Novelty Search},
author = {Phillip Smith and Robert Hunjet and Aldeida Aleti and Asad Khan},
journal= {arXiv preprint arXiv:1910.12412},
year = {2019}
}