The Tartarus Problem is one of the candidate benchmark problems in evolutionary algorithms. We take advantage of the graphical processing unit (GPU) to improve the results of the software agents that use finite state machines (FSMs) for this benchmark. While doing so we also contribute to the study of the problem on several grounds. Similar to existing studies we use genetic algorithms to evolve FSMs, but unlike most of them we use adaptive operators for controlling the parameters of the algorithm. We show that the actual number of valid boards is not 297,040, but 74,760, because the agent is indifferent to the rotations of the board. We also show that the agent can only come across 383 different combinations, rather than 6561 that is used in the current literature. A final contribution is that we report the first true scores for the agents by testing them with all available 74,760 boards. Our best solution has a mean score of 8.5379 on all boards.

Tartarus问题是进化算法中候选基准问题之一。我们利用图形处理单元（GPU）来改善使用有限状态机（FSM）进行此基准测试的软件代理的结果。在这样做的同时，我们还从几个方面为研究问题做出了贡献。与现有研究相似，我们使用遗传算法来进化FSM，但是与大多数研究不同，我们使用自适应算子来控制算法的参数。我们显示，有效代理的实际数量不是297,040，而是74,760，因为代理对纸板的轮换无所谓。我们还表明，该代理只能遇到383种不同的组合，而不是当前文献中使用的6561。最后的贡献是，我们通过对所有可用的74,760个板进行测试，以报告代理商的第一个真实分数。我们最好的解决方案在所有主板上的平均得分为8.5379。