Theory of parameter control for discrete black-box optimization: provable performance gains through dynamic parameter choices

Abstract

Parameter control is aimed at realizing performance gains through a dynamic choice of the parameters which determine the behavior of the underlying optimization algorithm. In the context of evolutionary algorithms, this research line has for a long time been dominated by empirical approaches. With the significant advances in running-time analysis achieved in the last ten years, the parameter control question has become accessible to theoretical investigations. A number of running-time results for a broad range of different parameter control mechanisms have been obtained in recent years. This chapter surveys these results, and puts them into context by proposing an updated classification scheme for parameter control.