A Parallel Island Genetic Algorithm for Triangle-based Image Reconstruction

In this paper, we investigate the application of Genetic Algorithms (GAs) and their parallelization via the Island Model (IMGA) for polygonal image reconstruction. The objective is to approximate a target image using a fixed number of colored triangles, formulating the problem as a high-dimensional optimization challenge. A standard GA is first implemented to assess its effectiveness using various objective functions. We then introduce a parallel IMGA framework, featuring multiple subpopulations evolving independently with periodic migration in a ring topology. Migration parameters are calibrated, revealing optimal settings for preserving diversity and improving convergence. Experimental results indicate that IMGA achieves up to a 50% improvement in reconstruction quality compared to the standard GA, with minimal additional computational cost. Moreover, our approach significantly outperforms the current state of the art for this problem, which relies on a hybrid method combining genetic algorithms and machine learning. These findings highlight the effectiveness and scalability of island models in addressing complex, high-dimensional optimization problems such as image reconstruction.