Abstract
This research work intends to elucidate Traveling Salesman Problem, a variant of Symmetric Hamiltonian Cycle Problems more capably. A different mutation operator called m-mutation operator for genetic algorithm is proposed to solve this problem. The efficiency of proposed mutation operator is compared with existing mutation operators by retaining the same selection, crossover and fitness function. The mutation operator is tested with data from TSPLIB dataset. The intercity distance table of cities was input to the coded C program which implemented the proposed mutation operator. The same dataset was used to compare the performance of proposed mutation operator with other existing mutation operators. The results of the experiment confirm that proposed mutation operator searches better solutions quicker compared to other mutation operators.
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