Research On DNA-GA Algorithm Based On Tissue-like P System And Its Application In Clustering

Essentially,DNA-GA algorithm is a genetic algorithm based on DNA coding,which is a form of combination of evolutionary computing and DNA computing.Compared with the traditional binary coding,DNA-GA algorithm used the DNA coding method is more flexible.It can also carry out more genetic operations,which makes the DNA-GA algorithm express more genetic information.Therefore,DNA-GA can overcome some of the limitations of GA algorithm,such as the early convergence and the problem of binary Hamming cliff.Therefore,DNA-GA has been widely concerned by scholars in recent years.Now,the design of a more effective DNA-GA algorithm for human research has a strong theoretical and practical significance.Membrane computing,also known as P system,is a distributed parallel computing model abstracted from the functions and structures of biological cells,tissues or organs.From the point of view of computational efficiency,the P system can solve the NP problem in the linear time,so it can provide people with more convenience in computing intelligence.So far,membrane computing has been widely used in many fields,such as: computer science,biology,linguistics,approximate optimization,computer graphics,economics,cryptography and so on.Relative to the theoretical aspects,the application research of the membrane computing is still in the initial stage.The scholars expect the P system will have a breakthrough in the application areas.Clustering analysis is a technique of unsupervised learning.That is to say,it has an independent learning ability.The whole process of clustering can be described as follows: Each object in the whole data space is divided into different clusters according to the euclidean distance.The objects with closer distance will be divided into the same cluster,and the objects with farther distance will be divided intodifferent clusters,which eventually make objects in the same class as similar as possible and objects in different classes are as different as possible.With the development of cluster analysis,it has been widely used in the fields of pattern analysis,machine learning,data mining,document retrieval,image segmentation and pattern recognition.Based on the theory of the above-mentioned theory,a DNA-GA algorithm is ProPosed based on the tissue-like P system(TPDNA-GA)of membrane com Puting model.Mainly,this paper is involved in three Parts of innovation:Firstly,the genetic algorithm involved in the basic DNA-GA algorithm is modified in detail.And an improved DNA-GA algorithm based on the new reconstruction crossover operator is proposed.Second,the improved DNA-GA algorithm is combined with the tissue-like P system.The main purpose of the combination is to improve the performance of DNA-GA by using the large Parallelism and membrane rule of the tissue-type P system,which include the definition of the function and the improvement of membrane rules.So the algorithm can been used to find the oPtimal clustering results of the data sets waiting for processing.In this paper,three standard test functions are used to verify the performance of the proposed new algorithm.Thirdly,the relationship between TPDNA-GA and K-means is studied and compared.And the performance of the algorithm is analyzed by using the standard test sets.Finally,the clustering process of TPDNA-GA is applied to the Web documents.This paper presents a concrete application process of the documents clustering and uses the data in Reuters-21578 to verify and compare the clustering accuracy,which proves that the algorithm can provide convenience for people to query documents in their daily work.