Genetic Programming and Evolvable Machines

, Volume 17, Issue 3, pp 291–313

An automatic solver for very large jigsaw puzzles using genetic algorithms

  • Dror Sholomon
  • Omid E. David
  • Nathan S. Netanyahu
Article

DOI: 10.1007/s10710-015-9258-0

Cite this article as:
Sholomon, D., David, O.E. & Netanyahu, N.S. Genet Program Evolvable Mach (2016) 17: 291. doi:10.1007/s10710-015-9258-0

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.

Keywords

Computer vision Genetic algorithms Jigsaw puzzle 

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Dror Sholomon
    • 1
  • Omid E. David
    • 1
  • Nathan S. Netanyahu
    • 1
    • 2
  1. 1.Department of Computer ScienceBar-Ilan UniversityRamat GanIsrael
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

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