Hyper-parameter Tuning is among the most critical stages in building machine
learning solutions. This paper demonstrates how multi-agent systems can be
utilized to develop a distributed technique for determining near-optimal values
for any arbitrary set of hyper-parameters in a machine learning model. The
proposed method employs a distributedly formed hierarchical agent-based
architecture for the cooperative searching procedure of tuning hyper-parameter
values. The presented generic model is used to develop a guided randomized
agent-based tuning technique, and its behavior is investigated in both machine
learning and global function optimization applications. According the empirical
results, the proposed model outperformed both of its underlying randomized
tuning strategies in terms of classification error and function evaluations,
notably in higher number of dimensions.

本文演示了如何利用多智能体系统开发一个分布式技术，用于确定任意集合的超参数的近似最优值，并在机器学习和全局函数优化应用中进行了研究。研究表明，在更高的维度下，所提出的模型在分类误差和函数评估方面都优于其底层随机调优策略。