Parameter control in evolutionary optimisation

Abstract

Finding the global optimum of a complex function is one of the long-standing goals of applied mathematics and numerical analysis. Evolutionary algorithms have become a popular way of solving demanding and expensive optimisation problems. These algorithms are composed of several control parameters that need to be set, for the procedure of searching for the optimum of an objective function to be successful. Parameter setting is a challenging topic, since control parameter values affect significantly the performance of the algorithm. Moreover, the control parameters may interact with each other in an unpredictable way. On top of this, at different stages of optimisation process, different control parameters may be needed. In this chapter, we introduce some basic control parameters that can be modified and the main methods of parameter settings, i.e. parameter tuning (offline) and parameter control (online). More focus is given on parameter control and the three different strategies used to implement it: the deterministic, the adaptive and the self-adaptive parameter control. In addition, a short comparison between parameter tuning and parameter control is given, based on the current literature. The last section refers to the improvement that parameter control can bring in some of the most complex instances of real-world optimisation problems, such as dynamic or problems under uncertainty, when using EAs to solve them and some of the strategies that can be used in each instance.