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Algorithm selection and hyperparameter tuning are critical steps in bothacademic and applied machine learning. On the other hand, these steps arebecoming ever increasingly delicate due to the extensive rise in the number,diversity, and distributedness of machine learning resources. Multi-agentsystems, when applied to the design of machine learning platforms, bring aboutseveral distinctive characteristics such as scalability, flexibility, androbustness, just to name a few. This paper proposes a fully automatic andcollaborative agent-based mechanism for selecting distributedly organizedmachine learning algorithms and simultaneously tuning their hyperparameters.Our method builds upon an existing agent-based hierarchical machine-learningplatform and augments its query structure to support the aforementionedfunctionalities without being limited to specific learning, selection, andtuning mechanisms. We have conducted theoretical assessments, formalverification, and analytical study to demonstrate the correctness, resourceutilization, and computational efficiency of our technique. According to theresults, our solution is totally correct and exhibits linear time and spacecomplexity in relation to the size of available resources. To provide concreteexamples of how the proposed methodologies can effectively adapt and performacross a range of algorithmic options and datasets, we have also conducted aseries of experiments using a system comprised of 24 algorithms and 9 datasets.

Hybrid Algorithm Selection and Hyperparameter Tuning on Distributed Machine Learning Resources: A Hierarchical Agent-based Approach