Why Parameter Control Mechanisms Should Be Benchmarked Against Random Variation

Abstract—Parameter control mechanisms in evolutionary al-gorithms (EAs) dynamically change the values of the EA pa-rameters during a run. Research over the last two decades has delivered ample examples where an EA using a parameter control mechanism outperforms its static version with fixed parameter values. However, very few have investigated why such parameter control approaches perform better. In principle, it could be the case that using different parameter values alone is already sufficient and EA performance can be improved without sophisticated control strategies raising an issue in the methodology of parameter control mechanisms ’ evaluation. This paper investigates whether very simple random variation in parameter values during an evolutionary run can already provide improvements over static values. Results suggest that random variation of parameters should be included in the benchmarks when evaluating a new parameter control mechanism. I