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Genetic algorithm (GA) is an efficient tool for solving optimization problemsby 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 isconsidered the exploration operator of GA. Various mutation operators exist tosolve hard combinatorial problems such as the TSP. In this paper, we propose ahybrid mutation operator called "IRGIBNNM", this mutation is a combination oftwo existing mutations, a knowledge-based mutation, and a random-basedmutation. We also improve the existing "select best mutation" strategy usingthe proposed mutation. We conducted several experiments on twelve benchmarkSymmetric traveling salesman problem (STSP) instances. The results of ourexperiments show the efficiency of the proposed mutation, particularly when weuse it with some other mutations. Keyword: Knowledge-based mutation, Inversionmutation, Slide mutation, RGIBNNM, SBM.

Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness