graphical approach to the traveling salesman problem

This report details an approach to solving the Traveling Salesman Problem (TSP) using learning automata and a unique geometric approach. Two-dimensional Euclidean TSPs are considered and the type of learning automata used are commonly called neural networks. A standard neural net algorithm called back propagation proved to be fairly good at learning the sample figures, but a newer substitute for back propagation, called counter propagation, performed extremely well. An important goal of this research was to derive increased theoretical understanding of the TSP. This goal has been satisfied, especially with regard to instabilities in path length and the order of points traversed along the minimal path route. In addition, some applications to larger point problems are considered, and it is shown that configurations with isolated clusters of relatively closely spaced points relative to the convex hull apexes and the fixed points map quite well into the geometric figures presented here.