Dendritic Cell Algorithm (DCA) is a bio-inspired system which was specifically developed for anomaly detection problems. In its preprocessing phase, the conventional DC requires domain or expert knowledge to manually categorise the input features for a given dataset into three signal categories termed as safe signal, pathogenic associated molecular pattern and danger signal. The manual preprocessing phase often over-fits the data to the algorithm, which is undesirable. The principal component analysis (PCA) and fuzzy-rough set theory (FRST) based-DCA techniques have been proposed to overcome the aforementioned limitation by automatically categorising the input features to their convenient signal categories. However, the PCA destroys the underlying meaning behind the initial features presented in the input dataset and generates poor classification performance, whilst FRST-DCA is only practical for very simple datasets. Therefore, this study investigates the employment of Genetic Algorithm based on Partial Shuffle Mutation to automatically categorise the input features into the three signal categories. The experimental results of the proposed approach on eleven benchmark datasets have revealed its superiority over other versions of DCA in terms of accuracy, sensitivity and specificity.