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Hyper-parameter optimization is one of the most tedious yet crucial steps intraining machine learning models. There are numerous methods for this vitalmodel-building stage, ranging from domain-specific manual tuning guidelinessuggested by the oracles to the utilization of general-purpose black-boxoptimization techniques. This paper proposes an agent-based collaborativetechnique for finding near-optimal values for any arbitrary set ofhyper-parameters (or decision variables) in a machine learning model (orgeneral function optimization problem). The developed method forms ahierarchical agent-based architecture for the distribution of the searchingoperations at different dimensions and employs a cooperative searchingprocedure based on an adaptive width-based random sampling technique to locatethe optima. The behavior of the presented model, specifically against thechanges in its design parameters, is investigated in both machine learning andglobal function optimization applications, and its performance is compared withthat of two randomized tuning strategies that are commonly used in practice.According to the empirical results, the proposed model outperformed thecompared methods in the experimented classification, regression, andmulti-dimensional function optimization tasks, notably in a higher number ofdimensions and in the presence of limited on-device computational resources.

Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimization