The XRP Ledger (XRPL) relies on a Byzantine fault-tolerant consensus algorithm to ensure global agreement on transactions across distributed nodes. Despite its critical financial role, the implementation remains under-tested. While prior work has shown the potential of evolutionary testing to uncover potential consensus violations in XRPL, the role of genetic operator selection in this process remains unexplored. We address this research gap by presenting a comparative evaluation of four evolutionary configurations that differ in their balance of exploration and exploitation. The system is tested by injecting network delays to simulate adverse conditions and trigger violations. Our results show that the balance of exploration and exploitation affects the performance of bug detection: configurations that favor exploitation, complemented by subtle exploration, yield the most favorable results. In addition, we contribute an extensible testing method tailored to XRPL but applicable to other distributed systems.