Abstract There are many effective approaches that have been proposed for association rules mining (ARM) on binary or discrete-valued data. However, in many real-world applications, the data usually consist of numerical values and the standard algorithms cannot work or give promising results on these datasets. In numerical ARM (NARM), it is a difficult problem to determine which attributes will be included in the rules to be discovered and which ones will be on the left of the rule and which ones on the right. It is also difficult to automatically adjusting of most relevant ranges for numerical attributes. Directly discovering the rules without generating the frequent itemsets as used in the literature as the first step of ARM accelerates the whole process without determining the metrics needed for this step. In classical ARM algorithms, generally one or two metrics are considered. However, mined rules are needed to be comprehensible, surprising, interesting, accurate, confidential, and etc. in many real-world applications. Adjusting all of these processes without the need for the metrics to be pre-determined for each dataset seems another problem. For these purposes, evolutionary intelligent optimization algorithms seem potential solution method for this complex problem. In this paper, the performance analysis of seven evolutionary algorithms and fuzzy evolutionary algorithms; namely Alatasetal, Alcalaetal, EARMGA, GAR, GENAR, Genetic Fuzzy Apriori, and Genetic Fuzzy AprioriDC for NARM problem has been performed within eleven real datasets for the first time. The obtained results have also been compared with the classical Apriori algorithm to show the efficiencies of the intelligent algorithms on NARM problem. Performances of eight algorithms in terms of support, confidence, number of mined rules, number of covered records, and time metrics have been comparatively performed with eleven real-world datasets. One of the best-mined rules obtained by each algorithm has been given and analyzed with respect to confidence, support, and lift metrics.