Genetic Algorithm-Based Solver for Very Large Multiple Jigsaw Puzzles of Unknown Dimensions and Piece Orientation
This work addresses the challenge of automated puzzle assembly for applications in image processing or data reconstruction, though it appears incremental as it builds on existing genetic algorithm approaches.
The authors tackled the problem of solving very large multiple jigsaw puzzles with unknown dimensions and piece orientations using a genetic algorithm, achieving a new state-of-the-art in puzzle sizes solved and accuracy with significant improvements over prior methods.
In this paper we propose the first genetic algorithm (GA)-based solver for jigsaw puzzles of unknown puzzle dimensions and unknown piece location and orientation. Our solver uses a novel crossover technique, and sets a new state-of-the-art in terms of the puzzle sizes solved and the accuracy obtained. The results are significantly improved, even when compared to previous solvers assuming known puzzle dimensions. Moreover, the solver successfully contends with a mixed bag of multiple puzzle pieces, assembling simultaneously all puzzles.