An automatic solver for very large jigsaw puzzles using genetic algorithms

• Published in: Genetic programming and evolvable machines Vol. 17; no. 3; pp. 291 - 313
• Main Authors: Sholomon, Dror, David, Omid E., Netanyahu, Nathan S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2016
• Subjects: Artificial Intelligence; Biomedical Engineering and Bioengineering; Compilers; Computer Science; Crossovers; Electrical Engineering; Fitness; Genetic algorithms; Inspection; Interpreters; Jigsaw puzzles; Phases; Programming Languages; Programming Techniques; Segments; Software Engineering/Programming and Operating Systems; Solvers; Computer vision; Jigsaw puzzle; Genetic algorithms
• ISSN: 1389-2576; 1573-7632
• DOI: 10.1007/s10710-015-9258-0

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.