Termination detection strategies in evolutionary algorithms

Abstract

This paper provides an overview of developments on termination conditions in evolutionary algorithms (EAs). It seeks to give a representative picture of the termination conditions in EAs over the past decades, segment the contributions of termination conditions into progress indicators and termination criteria. With respect to progress indicators, we consider a variety of indicators, in particular in convergence indicators and diversity indicators. With respect to termination criteria, this paper reviews recent research on threshold strategy, statistical inference, i.e., Kalman filters, as well as Fuzzy methods, and other methods. Key developments on termination conditions over decades include: (i) methods of judging the algorithm's search behavior based on statistics, and (ii) methods of detecting the termination based on different distance formulations.