Many algorithms have been proposed for Association Rules Mining (ARM), like Apriori. However, such algorithms often have a downside for real word use: they rely on users to set two parameters manually, namely minimum Support and Confidence. In this paper, we propose Association Rules Mining by improving the Imperialism Competitive Algorithm (ARMICA), a novel ARM method, based on the heuristic Imperialism Competitive Algorithm (ICA), for finding frequent itemsets and extracting rules from datasets, whilst setting support automatically. Its structure allows for producing only the strongest and most frequent rules, in contrast to many ARM algorithms, thus alleviating the need to define minimum support and confidence. Experimental results indicate that ARMICA generates accurate rules faster than Apriori.