In this paper we propose the first effective automated, genetic algorithm
(GA)-based jigsaw puzzle solver. We introduce a novel procedure of merging two
"parent" solutions to an improved "child" solution by detecting, extracting,
and combining correctly assembled puzzle segments. The solver proposed exhibits
state-of-the-art performance solving previously attempted puzzles faster and
far more accurately, and also puzzles of size never before attempted. Other
contributions include the creation of a benchmark of large images, previously
unavailable. We share the data sets and all of our results for future testing
and comparative evaluation of jigsaw puzzle solvers.

本文介绍了基于遗传算法的拼图解决方案，并提出了一种新颖的合并两个 “父” 解决方案的程序，这些父方案是通过检测、提取和组合正确装配的拼图段而得到的，以获得一个改进的 “子” 解决方案。该算法的性能是目前最先进的，可以更快地和更准确地解决以前尝试过的拼图，也可以解决以前从未尝试过的尺寸的拼图。此外，本文还创建了以前不可用的大型图像基准，共享数据集和所有结果以供未来测试、比较评估拼图解决方案。