The decentralized nature of blockchain systems makes them prone to concurrency bugs, which are difficult to detect. There exist testing techniques to find these bugs, such as systematic exploration of the solution space, but these techniques are difficult to scale. Evolutionary algorithms have been proposed as an effective solution to find these bugs. In this research, we aim to discover the influence of evolutionary operators in the bug detection performance of evolutionary algorithms. We test this on the XRP Ledger Consensus Protocol (XRP LCP) using priority-based event representation. We present Groot, an evolutionary algorithm that is implemented using a modified version of the Rocket framework. We experimented with two combinations of operators: the Simulated Binary Crossover (SBX) operator with the Gaussian mutation operator and the Laplace Crossover (LX) operator with the Makinen, Periaux and Toivanen Mutation (MPTM) operator. We evaluated these setups using effectiveness and efficiency and compared them to a random baseline. We used a bug-seeded version of the XRP LCP to run the experiments of these setups. We discovered that all setups are capable of detecting bugs in the XRP LCP. The results indicate that the effectiveness and efficiency is not influenced by the choice of these operators in a significant way. We discuss that possible reasons for these discoveries include noise in the fitness function, event representation limitations and configuration choices that may have contributed to these results.