Searching Cliques in a Fuzzy Graph Based on an Evolutionary and Biological Method

In this paper, a new and systematic approach for the integration of fuzzy-based methods and biological computation, named as an evolutionary and biological method, is proposed for searching cliques in a fuzzy graph. When dealing with a number of nodes in a graph, the most intractable problem is often detecting the maximum clique, which is automatically obtained from finding a solution to the arranged cliques in descending order. The evolutionary and biological method is proposed to identify all the cliques and to arrange them in a fuzzy graph, and then to structure all the nodes in the graph, based on the searched cliques, in different hierarchical levels. This challenging approach, involving the integration of two techniques, provides a new and better method for solving clique problems.

[1]  M Hagiya,et al.  A DNA-based in vitroGenetic Program , 2002, Journal of biological physics.

[2]  Volker Stix,et al.  Approximating the maximum weight clique using replicator dynamics , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3]  Junzo Watada,et al.  DNA Computing and Its Applications , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[4]  S.-C. Cheng,et al.  Cliques and fuzzy cliques in fuzzy graphs , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[5]  Junzo Watada,et al.  BIO-INSPIRED EVOLUTIONARY METHOD FOR CABLE TRENCH PROBLEM , 2007 .

[6]  M. Hagiya,et al.  State transitions by molecules. , 1999, Bio Systems.

[7]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[8]  David I. Lewin,et al.  DNA computing , 2002, Comput. Sci. Eng..

[9]  Laura F. Landweber,et al.  Towards a Re-programmable DNA Computer , 2003, DNA.

[10]  P D Kaplan,et al.  DNA solution of the maximal clique problem. , 1997, Science.