Improving Evolutionary Algorithms in a Continuous Domain by Monitoring the Population Midpoint

It is advocated that monitoring the population midpoint allows for improving the efficiency of population-based evolutionary algorithms (EAs) in $\mathbb {R}^{ d}$ . The theoretical motivation supporting this hypothesis is provided in this letter, and this phenomenon is empirically confirmed for selected typical EAs by a series of tests for fitness functions contained in the CEC2005 and CEC2013 benchmark sets.