  Genetic algorithm (GA) is an efficient tool for solving optimization problems
by evolving solutions, as it mimics the Darwinian theory of natural evolution.
The mutation operator is one of the key success factors in GA, as it is
considered the exploration operator of GA. Various mutation operators exist to
solve hard combinatorial problems such as the TSP. In this paper, we propose a
hybrid mutation operator called "IRGIBNNM", this mutation is a combination of
two existing mutations, a knowledge-based mutation, and a random-based
mutation. We also improve the existing "select best mutation" strategy using
the proposed mutation. We conducted several experiments on twelve benchmark
Symmetric traveling salesman problem (STSP) instances. The results of our
experiments show the efficiency of the proposed mutation, particularly when we
use it with some other mutations. Keyword: Knowledge-based mutation, Inversion
mutation, Slide mutation, RGIBNNM, SBM.
