Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolutionary Algorithms, have recently managed to beat the state of the art in win rate across many video games. However, the best results in a game are highly dependent on the specific configuration of modifications and hybrids introduced over several papers, each adding additional parameters to the core algorithm. Further, the best previously published parameters have been found from only a few human-picked combinations, as the possibility space has grown beyond exhaustive search. This paper presents the state of the art in Rolling Horizon Evolutionary Algorithms, combining all modifications described in literature, as well as new ones, for a large resultant hybrid. We then use a parameter optimiser, the N-Tuple Bandit Evolutionary Algorithm, to find the best combination of parameters in 20 games from the General Video Game AI Framework. Further, we analyse the algorithm's parameters and some interesting combinations revealed through the optimisation process. Lastly, we find new state of the art solutions on several games by automatically exploring the large parameter space of RHEA.

中文翻译：

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用于一般视频游戏的滚动地平线进化算法

玩游戏的进化算法，特别是滚动地平线进化算法，最近在许多视频游戏中成功击败了最先进的赢率。然而，游戏中的最佳结果高度依赖于几篇论文中引入的修改和混合的特定配置，每篇论文都向核心算法添加了额外的参数。此外，由于可能性空间已经超出了穷举搜索的范围，因此仅从几个人工选择的组合中找到了先前发布的最佳参数。本文介绍了滚动地平线进化算法的最新技术，结合了文献中描述的所有修改以及新的修改，用于大型合成混合。然后我们使用参数优化器，N-Tuple Bandit Evolutionary Algorithm，从通用视频游戏 AI 框架中找到 20 款游戏的最佳参数组合。此外，我们分析了算法的参数和通过优化过程揭示的一些有趣的组合。最后，我们通过自动探索​​ RHEA 的大参数空间，在几个游戏中找到了最先进的新解决方案。