  In this paper, we revisit the application of Genetic Algorithm (GA) to the
Traveling Salesperson Problem (TSP) and introduce a family of novel crossover
operators that outperform the previous state of the art. The novel crossover
operators aim to exploit symmetries in the solution space, which allows us to
more effectively preserve well-performing individuals, namely the fitness
invariance to circular shifts and reversals of solutions. These symmetries are
general and not limited to or tailored to TSP specifically.
