Partitional Clustering is one of the major techniques in Unsupervised Learning in which similar data are put into the same partition. Besides partitioning the unlabeled data, determining the optimal number of partitions is also another main concern in the field of data clustering. Automatic Clustering Differential Evolution (ACDE) is one of the state-of-the-art algorithms that address this concern. In ACDE, the mechanism to determine the optimal number of clusters is by encoding the activation value of each cluster centroid into the chromosome with fixed threshold value. However, it could be argued that a fixed threshold value would be seen as arbitrary, but a varying and adaptive threshold value could yield a solution that would better reflect the quality of clusters. In this paper, a new changing schema of threshold values is introduced for adaptively activating the clusters in the chromosomes, and a heuristic approach is implemented for adjusting the threshold values of each cluster according to their individual quality measurements. The results of several experiments show that the proposed algorithm performed generally better than other state-of-the-art automatic evolutionary clustering algorithms.