Swarm Behaviour Evolution via Rule Sharing and Novelty Search
This work addresses the challenge of designing robust swarm behaviors for complex environments, though it is incremental as it builds on previous research.
The authors tackled the problem of evolving swarm behaviors by hybridizing novelty search with a rule-sharing technique, resulting in SLCS2, which outperformed human-designed behaviors in data-transfer tasks across various environmental conditions.
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.