We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine-learning (ML) models, focusing on evolutionary agents in Gymnasium (game) simulators -- where fitness computation is costly. Maintaining a dataset of sampled individuals along with their actual fitness scores, we continually update throughout an evolutionary run a fitness-approximation ML model. We compare different methods for: 1) switching between actual and approximate fitness, 2) sampling the population, and 3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than that of the fully run GA -- depending on the ratio of approximate-to-actual-fitness computation. Our approach is generic and can be easily applied to many different domains.

翻译：我们提出了一种新颖方法，利用机器学习（ML）模型在遗传算法（GA）中执行适应度近似，重点关注Gymnasium（游戏）模拟器中计算代价高昂的进化智能体。通过维护一个包含采样个体及其实际适应度得分的数据集，我们在进化过程中持续更新适应度近似的机器学习模型。我们比较了以下三种方法的不同实现：1）实际适应度与近似适应度之间的切换策略，2）种群采样方法，3）样本加权方案。实验结果表明，该方法显著提升了进化运行时间，同时获得的适应度得分与完全运行遗传算法的结果相当或略低——具体取决于近似适应度计算与实际适应度计算的比例。该方法是通用性的，可轻松应用于多个不同领域。