Enhanced Metaheuristic Based Hyper-Parameters Tuning for Learning Models

Hyperparameter tuning is a crucial step in the process of building accurate machine learning models. Finding the optimal combination of hyperparameters can be challenging, especially in complex models with large hyperparameter spaces. Genetic algorithms (GAs) have become a popular approach to address this challenge by efficiently exploring the hyperparameter space and selecting the best combination. In the paper, different machine learning models are used along with the genetic search algorithms for the prediction of multi-diseases. The purpose of using the genetic algorithm is to optimize the learning models hyper-parameters. Based upon the evaluations we come to know which models performed well after hyper-parameter optimization using meta heuristic optimization.