On Clustering in Evolutionary Computation

When the fitness landscape exhibits a multi-modal property, clustering plays a key role in the evolutionary computation, because clusters explicitly or implicitly denote optima present. Correct clusters result in effective and efficient evolution. In this paper, a novel clustering strategy, called recursive middling (RM), is proposed. With acceptable overhead, RM effectively overcomes pitfalls of other popular clustering techniques, i.e. those based on Euclidean distance or Hill-Valley function. RM also dramatically enhances the performance of the selected evolutionary algorithm - dynamic niche clustering (DNC), by forming clusters centered around potential optima quickly and stably. The success rate and the number of optima found are both increased dramatically, compared to the original version of DNC.