In classification, feature selection is an essential preprocessing step that selects a small subset of features to improve classification performance. Existing feature selection approaches can be divided into three main approaches: wrapper approaches, filter approaches, and embedded approaches. In comparison with the two other approaches, embedded approaches usually have better trade-off between classification performance and computation time. One of the most well-known embedded approaches is sparsity regularisation-based feature selection which generates sparse solutions for feature selection. Despite its good performance, sparsity regularisation-based feature selection outputs only a feature ranking which requires the number of selected features to be predefined. More importantly, the ranking mechanism introduces a risk of ignoring feature interactions which leads to the fact that many top-ranked but redundant features are selected. This work addresses the above problems by proposing a new representation that considers the interactions between features and can automatically determine an appropriate number of selected features. The proposed representation is used in a differential evolutionary (DE) algorithm to optimise the feature subset. In addition, a novel initialisation mechanism is proposed to let DE consider various numbers of selected features at the beginning. The proposed algorithm is examined on both synthetic and real-world datasets. The results on the synthetic dataset show that the proposed algorithm can select complementary features while existing sparsity regularisation-based feature selection algorithms are at risk of selecting redundant features. The results on real-world datasets show that the proposed algorithm achieves better classification performance than well-known wrapper, filter, and embedded approaches. The algorithm is also as efficient as filter feature selection approaches.