机器学习辅助的智能体模型校准：代理优化结合遗传算法与粒子群优化
原标题：Machine learning-assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimization
AI 摘要
该研究将机器学习代理模型嵌入遗传算法和粒子群优化的迭代搜索循环中，用于基于智能体模型（ABM）的校准。代理模型每代筛选候选参数，模拟器仅验证排名靠前的候选，从而降低昂贵的黑箱模拟开销。在Brock-Hommes资产定价模型和Island增长模型上，该方法相比无代理基线将参数恢复RMSE分别降低20.0%和63.8%，速度分别提升32.1%和61.1%。
正文节选
Machine learning–assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimization Abstract Calibrating an agent-based model (ABM) is difficult because its objective landscape is stochastic and rugged, and can be evaluated only through costly black-box simulations. This study adapts inner-loop surrogate-assisted evolutionary computation (SAEC) to ABM calibration by embedding a machine-learning surrogate within genetic algorithm (GA) a