Swarm Behaviour Evolution via Rule Sharing and Novelty Search

TL;DR

This paper introduces SLCS2, a robust swarm behaviour evolution algorithm that combines rule sharing and novelty search, outperforming human-designed behaviors in complex environments.

Contribution

The paper presents SLCS2, a novel hybrid algorithm that enhances swarm behaviour evolution through rule sharing and novelty search, improving robustness and environmental adaptability.

Findings

SLCS2 outperforms human behaviour in data-transfer tasks.

Tailoring rule generators causes over-fitting.

General rule generators enhance environmental flexibility.

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.