Automated Parameter Tuning for Steering Algorithms

We propose a statistical framework and a methodology for automatically characterizing the influence that a steering algorithm’s parameters have on its performance. Our approach uses three performance criteria: the success rate of an algorithm in solving representative scenarios, the quality of the simulations solution, and the algorithm’s computational efficiency. Given an objective defined as a weighted combination of the performance criteria, we formulate an optimization problem in the space of the algorithm’s parameters that we solve with an evolutionarybased approach. Although our framework can analyze an algorithm from many perspectives, we present two demonstrative studies: a univariate analysis that studies the effects of each of the algorithm’s parameters in isolation, and a multivariate analysis that studies the combined effect of the algorithm’s parameters on the objectives.