ML can guide optimization processes by predicting the most promising regions of the search space, thereby reducing the number of evaluations needed to find optimal solutions.

Hybrid algorithms can use ML to adaptively adjust their parameters based on the problem's characteristics and the current state of the search, leading to more efficient and effective solutions.

ML techniques can be employed to select and engineer features that are most relevant to the problem at hand, improving the performance of the hybrid algorithm.

ML models can capture complex, non-linear relationships within the data that traditional optimization methods might miss, enhancing the overall performance of the hybrid approach.

Hi F. Adnan,

Maybe this paper can help you Article Hybrid approaches to optimization and machine learning metho...