Detecting change in dynamic fitness landscapes

Change detection enables an evolutionary algorithm operating in a dynamic environment to respond with undertaking necessary steps for maintaining its performance. We consider two major types of change detection, population-based and sensor-based. For population-based we show its relation to statistical hypothesis testing and analyze it using receiver-operating characteristics. For sensor-based the relationship between detection success and number of employed sensors is studied and the dimensionality problem is addressed. Finally, we discuss how both types of change detection compare to each other.