An efficient clustering strategy for estimation of distribution algorithms (EDAs) is presented. It is used for properly fitting probabilistic models that play an important role in guiding search direction. To this end, a fitness-aided ordering scheme is devised for deciding the input sequence of samples (i.e., individuals) for clustering. It can effectively categorise the individuals by using the (available) information about fitness landscape. Moreover, a virtual leader is introduced for providing a reliable reference for measuring the distance from samples to its own cluster. The proposed algorithm incorporates them within the framework of random the leader algorithm (RLA). Experimental results demonstrate that the proposed approach is more effective than the existing ones with regard to probabilistic model fitting.

This paper deals with questions concerning the supply of building-blocks (BBs) in the initial population of real-coded genetic algorithms (rGAs). Drawing upon the methodology of existing BB supply studies for finite alphabets, facetwise models for the supply of a single schema as well as for the supply of all the schemata in a partition are proposed. A model for the initial population size necessary to ensure the presence of all the raw BBs with a given supply error has also been developed using the partition success model. Experimental results show the effectiveness of the facetwise models and the initial population sizing model. Finally, an adaptation approach is suggested for practical use of the BB supply.

This paper proposes a genetically inspired adaptive clustering algorithm for numerical and categorical data sets. To this end, unique encoding method and fitness functions are developed. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster-purity. Moreover, it outperforms existing clustering algorithms.

This paper presents a new algorithm for efficiently detecting silhouette voxels in volume objects. The high performance of the algorithm is partly due to its ability to exclude all the gradient vectors not associated with silhouettes from further consideration. A judicious re-arrangement of the voxels enhances its efficiency. We have studied its performance through computer simulations. The results indicate a manifold improvement over conventional algorithms. A parallel version of the algorithm has also been described in the paper. Its performance is quite understandably impressive.