Federated learning (FL) enables collaborative training across distributed clients without directly sharing their raw data. However, non-IID data distributions and malicious behaviors can produce unreliable and conflicting client updates, severely degrading aggregation stability and global generalization. Existing robust aggregation methods often depend on additional trust assumptions, server-side clean data, or expensive pairwise update comparisons, which not only limit their practicality but also increase computational and resource overhead in real-world FL scenarios. To address these limitations, we propose FedRGEA, a reliability-guided bio-inspired evolutionary aggregation framework for resource-aware robust federated aggregation. FedRGEA integrates self-calibrated reliability assessment (SCRA) with reference-guided conflict-aware evolutionary aggregation (RCEA). Specifically, SCRA constructs dynamic references from client updates themselves and evaluates update reliability through multi-criteria deviation analysis without relying on externally trusted references. The resulting reliability priors are incorporated into RCEA, which constructs a refined reference from the retained updates and formulates robust aggregation as a simplex-constrained evolutionary weight search problem by jointly considering reliability priors, reference-based conflicts, and weight concentration. Experimental results show that FedRGEA outperforms representative FL and robust aggregation baselines under the 40% sign-flipping attack, attaining accuracies of 77.88%, 83.97%, and 97.92% on ISIC 2019, HAM10000, and BrainTumor, respectively. These results demonstrate the effectiveness of FedRGEA in maintaining robust aggregation under a high proportion of malicious clients.