We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine-learning (ML) models, through dynamic adaptation to the evolutionary state. Maintaining a dataset of sampled individuals along with their actual fitness scores, we continually update a fitness-approximation ML model throughout an evolutionary run. 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. Although we focus on evolutionary agents in Gymnasium (game) simulators -- where fitness computation is costly -- our approach is generic and can be easily applied to many different domains.

翻译：我们提出了一种新颖的适应度近似方法，该方法在遗传算法（GAs）中利用机器学习（ML）模型，通过动态适应进化状态来实现。通过维护一个包含采样个体及其实际适应度分数的数据集，我们在整个进化过程中持续更新适应度近似ML模型。我们比较了不同方法：（1）在实际适应度与近似适应度之间进行切换；（2）对种群进行采样；（3）对样本进行加权。实验结果表明，进化运行时间显著缩短，且适应度分数与完全运行GA的结果相同或略低——具体取决于近似适应度与实际适应度计算的比例。尽管我们专注于Gymnasium（游戏）模拟器中的进化智能体——其中适应度计算成本高昂——但我们的方法具有通用性，可轻松应用于许多不同领域。