Tissue-like P system based DNA-GA for clustering

In recent years, DNA GA algorithm is drawing attention from scholars. The algorithm combines the DNA encoding and Genetic Algorithm, which solve the premature convergence of genetic algorithms, the weak local search capability and binary Hamming cliff problems effectively.How to design a more effective way to improve the performance of DNA-GA algorithm is more worth studying. As is known to all,the tissue-like P system can search for the optimal clustering partition with the help of its parallel computing advantage effectivel. This paper is under this premise and presents DNA-GA algorithm based on tissue-like P systems (TPDNA-GA) with a loop structure of cells, which aims to combine the parallelism and the evolutionary rules of tissue-like P systems to improve performance of the DNA-GA algorithm. The objective of this paper is to use the TPDNA-GA algorithm to support clustering in order to find the best clustering center.This algorithm is of particular interest to when dealing with large and heterogeneous data sets and when being faced with an unknown number of clusters. Experimental results show that the proposed TPDNA-GA algorithm for clustering is superior or competitive to classical k-means algorithm and several evolutionary clustering algorithms.