Requirements monitoring continuously probes system components and sensors to assess whether the system satisfies its requirements and to detect conditions conducive to a requirement violation. Monitoring, however, is typically computationally expensive, intrusive, and presents tradeoffs between monitoring costs and accuracy, or the degree of coverage and coherence of gathered data. As such, it may be desirable to adapt the monitoring behavior of a software system in response to changing system and environmental conditions, as these may differ from those anticipated at design time. This paper presents Plato-RE, an evolutionary computation-based approach for adaptively monitoring the satisfaction of requirements. In particular, Plato-RE detects conditions conducive to a requirements violation and generates, at run time, new monitoring configurations to minimize monitoring costs and maximize monitoring accuracy. Each generated configuration specifies the frequencies for gathering data from each monitoring component and sensor. We applied Plato-RE to dynamically adapt the monitoring behavior of a simulated mobile robot that must track and navigate towards a mobile target while avoiding collisions. Preliminary results suggest Plato-RE is capable of adapting the requirements monitoring behavior of a system to minimize monitoring costs while maximizing accuracy.