Accelerating heuristic convergence on the "Evolution of Mona Lisa" problem by including image-centric mutation operators

Abstract

The "Evolution of Mona Lisa" problem aims to approximate a target image by overlapping many semi-transparent polygons. The problem has been tackled in the past using multiple Nature-Inspired heuristics, and our main contribution is adding image-centric mutation operators (scaling, rotating and translating polygons).We compare Genetic Algorithms, Hill-Climbing and Simulated Annealing. A candidate solution has variable length (of, at most, 300 decagons) and, due to the variable opacity of polygons, order matters – resulting, in practice, in a pseudo-Messy GA. We use the same representation and mutation operators for the trajectory methods which, due to the focus on wall-clock time, outperform our GA implementation.We find that these methods retain good image approximation at good run times: 98.9-99.2% (mean on 30 images), with a time limit of 30 minutes, on images 500-pixels tall.