Studying feedback mechanisms for adaptive parameter control in evolutionary algorithms

The performance of an Evolutionary Algorithm (EA) is greatly affected by the settings of its strategy parameters. An effective solution to the parameterisation problem is adaptive parameter control, which applies learning methods that use feedback from the optimisation process to evaluate the effect of parameter value choices and adjust the parameter values over the iterations. At every iteration of an EA, the performance of an EA is reported and employed by the feedback mechanism as an indication of the success of the parameterisation of the algorithm instance. Many approaches to collect information about the algorithm's performance exist in single objective optimisation. In this work, we review the most recent and prominent approaches. In multiobjective optimisation, establishing a single scalar which can report the algorithm's performance as feedback for adaptive parameter control is a complex task. Existing performance measures of multiobjective optimisation are generally used as feedback for the optimisation process. We discuss the properties of these measures and present an empirical evaluation of the binary hypervolume and ϵ+-indicators as feedback for adaptive parameter control.