True scores for tartarus with adaptive GAs that evolve FSMs on GPU

Abstract

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. (C) 2020 Elsevier Inc. All rights reserved.