This work aims at solving the Traveling Salesman Problem (TSP) through developing an advanced intelligent technique based on real Genetic Algorithm (GA). The used GA comprises real-value coding with specific behavior taking each code as it is (whether binary, integer, or real), rank selection, and efficient uniform genetic operators. The results indicated, in comparison with the other applied optimization methods (linear, dynamic, Monte Carlo and heuristic search methods), that the real GA produces significantly the lowest distance (least cost tour) solution. It is concluded that the real GA approach is robust and it represents an efficient search method and is easily applied to nonlinear and complex problems of the TSP in the field of solid waste routing system in the large cities.
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