One of the challenging problems in solving optimization problems with evolutionary algorithms is the selection of the control parameters, which allow to adjust the behaviour of the algorithms to the problem at hand. Several control parameters need to be set, for the procedure of searching for the optimum of an objective function to be successful. Suitable control parameter values need to be found, for example, for the population size, the mutation strength, the crossover rate, the selective pressure, etc. The choice of these parameters can have a significant impact on the performance of the algorithm and need thus to be executed with care.
With parameter control approach no prior training of parameters is needed. It also accounts for the fact that the optimal parameter values typically change during the optimization process: for example, at the beginning of an optimization process we typically aim for exploration, while in the later stages we want the algorithm to converge and to focus its search on the most promising regions in the search space.
While parameter control is indispensable in continuous optimization, it is far from being well-established in discrete optimization heuristics. The ambition of this tutorial is to inform participants about different parameter control techniques, and by discussing both theoretical as well as experimental results that demonstrate the unexploited potential of non-static parameter choices.
Our tutorial addresses experimentally and theory-oriented researchers alike, and requires only basic knowledge of optimization heuristics.