From within the variety of research that has been devoted to the adaptation of Differential Evolution to the solution of problems dealing with permutation variables, the Geometric Differential Evolution algorithm appears to be a very promising strategy. This approach is based on a geometric interpretation of the evolutionary operators and has been specifically proposed for combinatorial optimization. Such an approach is adopted in this paper, in order to evaluate its efficiency on a challenging class of combinatorial optimization problems: the Job-Shop Scheduling Problem. This algorithm is implemented and tested on a selection of instances normally adopted in the specialized literature. The results obtained by this approach are compared with respect to those generated by a classical DE implementation (using Random Keys encoding for the decision variables). Our computational experiments reveal that, although Geometric Differential Evolution performs (globally) as well as classical DE, it is not really able to significantly improve its performance.
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