Grid search is the most effective method for tuning hyperparameters in machine learning (ML). However, it has high computational complexity, and is not appropriate when here are many hyperparameters to be tuned. In this paper, two novel suboptimal grid search methods are presented, which search the grid marginally and alternatively. In order to show the efficiency of hyperparameter tuning by the proposed methods four datasets are used. Two datasets were collected by simulating FSO and fiber OC links by MATLAB software, and two other datasets were collected by experimental setups for FSO and fiber OC links in Optisystem software. Results indicate that despite greatly reducing computational complexity, the proposed methods achieve a favorable performance. The proposed structures are compared with some of the recently published most relevant works, and the efficiency of the proposed methods is proved.

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