Iteration-wise parameter learning

Adjusting the control parameters of population-based algorithms is a means for improving the quality of these algorithms' result when solving optimization problems. The difficulty lies in determining when to assign individual values to specific parameters during the run. This paper investigates the possible implications of a generic and computationally cheap approach towards parameter analysis for population-based algorithms. The effect of parameter settings was analyzed in the application of a genetic algorithm to a set of traveling salesman problem instances. The findings suggest that statistics about local changes of a search from iteration i to iteration i + 1 can provide valuable insight into the sensitivity of the algorithm to parameter values. A simple method for choosing static parameter settings has been shown to recommend settings competitive to those extracted from a state-of-the-art parameter tuner, paramlLS, with major time and setup advantages.