Technological advancement in all the fields leads to the problem of information overload in front of internet user. It has become extremely difficult for them to reach to the information which is most relevant to their need. Information gigantic size makes the user to wander from one web page to another in order to reach to their target information. This leads to wastage of user time and also reduces the interest of user from the search engine, websites as well as internet. The problem of accessing user relevant web pages falls into the category of NP-Complete problems. Web Mining, an application of data mining is utilized to find the solution of this issue of information extraction. For retrieving the relevant data top T web links needs to be prioritized. Here we propose a memetic algorithm and simulated annealing algorithm for selecting the most relevant web document. Both the algorithms are compared on the basis of their performance experimentally and results shows the domination of one over another.

Web Mining, NP-complete, Memetic algorithm, Simulated Annealing algorithm

[1] S. Sharma, and M. Rai, “Customer Behavior Analysis using Web Usage Mining,” International Journal of Scientific Research in Computer Science and Engineering, vol. 5, issue 6, pp. 47-50, 2017.
[2] A. Kashyap, I. Naseem, and D. Mandloi, "Web Mining an Approach to Evaluate Web", International Journal of Scientific Research in Computer Science and Engineering, vol. 5, issue 3, pp. 79-85, 2017.
[3] R. Cooley, B. Mobasher, and J. Srivastava, “Web mining: information and pattern discovery on the World Wide Web,” pp. 558–567, 2002.
[4] R. Kosala and H. Blockeel, “Web Mining Research: A Survey,” vol. 2, no. 1, 2000.
[5] O. R. Za, “10.1.1.21.799.Pdf.”
[6] S. Ajoudanian and M. D. Jazi, “Deep Web Content Mining,” vol. 3, no. 1, pp. 501–505, 2009.
[7] U. shi and M. R. Singh, “Page Content Rank: An Approach to the Web Content Mining,” Int. J. Eng. Trends Technol., vol. 22, no. 2, pp. 74–78, 2015.
[8] G. Poonkuzhali, K. Thiagarajan, K. Sarukesi, and G. V Uma, “Signed Approach for Mining Web Content Outliers,” vol. 3, no. 8, pp. 820–824, 2009.
[9] Cooley, R., Tan, P. N., & Srivastava, J. (1999, August). Websift: the web site information filter system. In Proceedings of the Web Usage Analysis and User Profiling Workshop (Vol. 8).
[10] A. Jimeno-Yepes, R. Berlanga-Llavori, and D. Rebholz-Schuhmann, “Ontology refinement for improved information retrieval,” Inf. Process. Manag., vol. 46, no. 4, pp. 426–435, 2010.
[11] F. M. Facca and P. L. Lanzi, “Recent Developments in Web Usage Mining Research,” pp. 140–150, 2003.
[12] C. C. Lin, “Optimal Web site reorganization considering information overload and search depth,” Eur. J. Oper. Res., vol. 173, no. 3, pp. 839–848, 2006.
[13] R. Fuller, R. John, R. B. Eds, P. Sincak, J. Vascak, and V. Kvasnicka, and Web Mining Advances in Soft Computing. .
[14] F. Neri and C. Cotta, “Memetic algorithms and memetic computing optimization: A literature review,” Swarm Evol. Comput., vol. 2, no. February, pp. 1–14, 2012.
[15] P. Moscato and C. Cotta, “A Modern Introduction to Memetic Algorithms,” no. January 2003, pp. 141–183, 2010.
[16] A. Orman, E. Aarts, and J. K. Lenstra, “Local Search in Combinatorial Optimisation.,” J. Oper. Res. Soc., vol. 50, no. 2, p. 191, 2006.
[17] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization of Simulated Annealing,” Ann. Phys. (N. Y)., vol. 54, no. 2, pp. 671–680, 1969.