In geo-replicated systems, the heterogeneous latencies of connections between replicas limit the system's ability to achieve consensus fast. State machine replication (SMR) protocols can be refined for their deployment in wide-area networks by using a weighting scheme for active replication that employs additional replicas and assigns higher voting power to faster replicas. Utilizing more variability in quorum formation allows replicas to swiftly proceed to subsequent protocol stages, thus decreasing consensus latency. However, if network conditions vary during the system's lifespan or faults occur, the system needs a solution to autonomously adjust to new conditions. We incorporate the idea of self-optimization into geographically distributed, weighted replication by introducing AWARE, an automated and dynamic voting weight tuning and leader positioning scheme. AWARE measures replica-to-replica latencies and uses a prediction model, thriving to minimize the system's consensus latency. In experiments using different Amazon EC2 regions, AWARE dynamically optimizes consensus latency by self-reliantly finding a fast weight configuration yielding latency gains observed by clients located across the globe.