  This paper proposes a hybrid genetic algorithm for solving the Multiple
Traveling Salesman Problem (mTSP) to minimize the length of the longest tour.
The genetic algorithm utilizes a TSP sequence as the representation of each
individual, and a dynamic programming algorithm is employed to evaluate the
individual and find the optimal mTSP solution for the given sequence of cities.
A novel crossover operator is designed to combine similar tours from two
parents and offers great diversity for the population. For some of the
generated offspring, we detect and remove intersections between tours to obtain
a solution with no intersections. This is particularly useful for the min-max
mTSP. The generated offspring are also improved by a self-adaptive random local
search and a thorough neighborhood search. Our algorithm outperforms all
existing algorithms on average, with similar cutoff time thresholds, when
tested against multiple benchmark sets found in the literature. Additionally,
we improve the best-known solutions for $21$ out of $89$ instances on four
benchmark sets.
