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We briefly review previous attempts to generate near-optimal solutions of the Traveling Salesman Problem by applying Genetic Algorithms. Following the lines of Johnson [1990] we discuss some possibilities for speeding up classical Local Search algorithms by casting them into a genetic frame. In an experimental study two such approaches, viz. Genetic Local Search with 2-Opt neighbourhoods and Lin-Kernighan neighbourhoods, respectively, are compared with the corresponding classical multi-start Local Search algorithms, as well as with Simulated Annealing and Threshold Accepting, using 2-Opt neighbourhoods. As to be expected a genetic organization of Local Search algorithms can considerably improve upon performance though the genetic components alone can hardly counterbalance a poor choice of the neighbourhoods.