AI based parameter estimation of ML model using Hybrid of Genetic Algorithm and Simulated Annealing

In this study, parameter estimation of ML models, here specifically, random forest classifier was conducted on a heart disease dataset. We have suggested a new approach to machine learning that hybridizes genetic algorithm with simulated annealing for estimating the hyperparameters of the random forest classifier. The workings of genetic algorithm are modeled after the process of natural selection to evolve a population of candidate solutions towards a better one and solve complex optimization problems effectively. Simulated Annealing is a probabilistic optimization technique, where the solution is iteratively improved by accepting moves that lead to a worse solution with a certain probability, this avoids getting stuck in local optima, and can handle optimization problems with many variables and noise. This application of hybrid optimization helped in increasing the accuracy of the machine learning model by 10% and is very promising in comparison to various different classification methods for this same problem.