Detection of the permutation symmetry in pattern sets

Abstract

Symmetry is a powerful tool to reduce the freedom degrees of a system. But the applicability of the symmetry tool strongly depends on the ability to calculate the symmetries of the system. There exists an interesting algorithmic problem to search for the symmetry of a high-dimensional system. In this paper, a genetic algorithm-based permutation symmetry detection approach is proposed for pattern set. Firstly, the permutation symmetry distance (PSD) is defined to measure the similarity of a pattern set before and after being transformed by a permutation operator. Secondly, the permutation symmetry detection problem is converted into an optimization problem by taking the PSD as a fitness function. Lastly, a genetic algorithm-based approach is designed for the symmetry detection problem. Computer simulation results are also given for five pattern sets of different dimensionality, which show the efficiency and speediness of the proposed detection approach, especially in high-dimensional cases.