Parameter tuning in an evolutionary algorithm for commodity transportation optimization

Tuning parameters of an evolutionary algorithm is the essential phase of a problem solving process since the parameter values significantly influence the algorithm efficiency. A traditional parameter tuning approach finds a setting of parameter values that is then used for solving various problem instances. Clearly, such parameter values may not perform well on specific problem instances. This paper suggests finding several parameter settings which are suitable for specific problem instances. However, this is not aimed at the level of each individual instance, but rather for specific types of problem instances. A new problem instance can then be solved using the tuned parameter values for its type. We demonstrate the approach by tuning parameters of an evolutionary algorithm for commodity transportation optimization with very heterogeneous problem instances. Numerical experiments show that the procedure improves the algorithm performance. Moreover, the analysis of empirical results reveals that there exist relations between the tuned parameter values and that they vary over types of problem instances.