Fitness Approximation

Fitness approximation focuses on efficiently estimating the performance (fitness) of solutions in complex optimization problems, particularly where direct fitness evaluation is computationally expensive. Current research emphasizes using machine learning models, including neural networks (e.g., transformers, VAEs) and reinforcement learning, to approximate fitness landscapes, often incorporating techniques like latent space optimization and phylogenetic analysis to improve search efficiency and diversity. These advancements are significantly impacting fields like protein engineering and procedural content generation by accelerating the discovery of high-performing solutions, and showing promise in applications such as personalized medicine through improved cardio-respiratory fitness prediction.