An Automatic Solver for Very Large Jigsaw Puzzles Using Genetic Algorithms
This work addresses the challenge of efficiently assembling large jigsaw puzzles, which is an incremental improvement in computational puzzle-solving for applications like image reconstruction.
The paper tackles the problem of solving very large jigsaw puzzles by proposing a genetic algorithm-based solver with a novel crossover procedure, achieving state-of-the-art performance on puzzles up to 30,745 pieces, which is larger than previous attempts of 22,755 pieces, and with faster concurrent implementation.
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.