Fitness Approximation through Machine Learning
Itai Tzruia Tomer Halperin Moshe Sipper Achiya Elyasaf
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.