Fitness Approximation through Machine Learning

arXiv:2309.03318v2 Announce Type: replace-cross
Abstract: 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 …

abstract algorithms approximation arxiv cs.ai cs.lg cs.ne dataset dynamic fitness machine machine learning novel replace state through type update