An automatic solver for very large jigsaw puzzles using genetic algorithms

Dror Sholomon, Omid E. David, Nathan S. Netanyahu

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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.

Original languageEnglish
Pages (from-to)291-313
Number of pages23
JournalGenetic Programming and Evolvable Machines
Issue number3
StatePublished - 1 Sep 2016

Bibliographical note

Publisher Copyright:
© 2016, Springer Science+Business Media New York.


  • Computer vision
  • Genetic algorithms
  • Jigsaw puzzle


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