Abstract: The Dendritic Cell Algorithm (DCA) is a classification algorithm inspired by the biological immune system that has shown promise for anomaly detection. However, the DCA relies on manual or domain expertise to tune signal weights which limits practical deployment and reproducibility. This paper addresses the DCA parameter optimisation challenge by applying the Clonal Selection Algorithm to optimise the weight matrix. The approach was evaluated using nine benchmark datasets achieving mean ROC-AUC of 96.48% compared to 66.56–75.33% for manually configured baselines (DCA-2 and DCA-1 individually, and best-of-the-two, respectively). CSA-DCA was also compared with alternative metaheuristic methods (GA-DCA: 92.25%; PSO-DCA: 90.83%), achieving numerically higher mean performance, though the difference was not statistically significant (Wilcoxon p = 0.082 vs. GA-DCA, p = 0.213 vs. PSO-DCA; small effect sizes, d = 0.34 and 0.41). The variance of CSA-DCA (CV = 7.88%) was lower than both manual baselines (CV > 35%), confirming more consistent performance across diverse problem domains.

Keywords: Dendritic Cell Algorithm, Clonal Selection Algorithm, metaheuristic optimisation, parameter tuning, anomaly detection, artificial immune systems, ROC-AUC

Pages: 405-413