Machine Learning and Genetic Algorithms: A case study on image reconstruction

In this research, we investigate the application of machine learning techniques to optimization problems and propose a novel integration between metaheuristics and machine learning for the problem of image reconstruction. We propose a modified version of the standard genetic algorithm that uses machine learning to quickly drive the search towards good solutions by dynamically adjusting its parameters. We conducted experiments to compare the performance of our proposed algorithm with other metaheuristic algorithms, including Tabu Search, Iterated Local Search, and Artificial Immune System. Our results demonstrate the effectiveness of our algorithm in finding better solutions and in achieving faster convergence times compared to the other algorithms. The significant computational time difference between the standard genetic algorithm and the genetic algorithm with machine learning highlights the innovation of our approach and its potential to improve real-world applications.