An Automatic Solver for Very Large Jigsaw Puzzles Using Genetic Algorithms

TL;DR

This paper presents a novel genetic algorithm-based solver for very large jigsaw puzzles, achieving state-of-the-art accuracy and efficiency on puzzles with up to 30,745 pieces, surpassing previous methods in size and performance.

Contribution

It introduces a new crossover procedure for GAs that effectively merges puzzle segments, enabling the solver to handle larger puzzles more accurately and efficiently than prior approaches.

Findings

Successfully solved puzzles with up to 30,745 pieces.

The new crossover operator improves solution quality.

Different fitness functions impact the solver's performance.

Abstract

In this paper we propose the first effective genetic algorithm (GA)-based jigsaw puzzle solver. We introduce a novel crossover procedure that merges two "parent" solutions to an improved "child" configuration by detecting, extracting, and combining correctly assembled puzzle segments. The solver proposed exhibits state-of-the-art performance, as far as handling previously attempted puzzles more accurately and efficiently, as well puzzle sizes that have not been attempted before. The extended experimental results provided in this paper include, among others, a thorough inspection of up to 30,745-piece puzzles (compared to previous attempts on 22,755-piece puzzles), using a considerably faster concurrent implementation of the algorithm. Furthermore, we explore the impact of different phases of the novel crossover operator by experimenting with several variants of the GA. Finally, we compare different fitness functions and their effect on the overall results of the GA-based solver.