Code World Models for Parameter Control in Evolutionary Algorithms
Summary
The paper investigates using world models to adaptively control parameters in evolutionary algorithms, aiming to improve convergence and efficiency. It outlines a framework where parameter settings are guided by learned world models, with empirical results suggesting benefits for automated optimization workflows in AI contexts.