Abstract
Searching on the Internet has grown in importance over the last few years, as huge amount of information is invariably accumulated on the Web. The problem involves locating the desired information and corresponding URLs on the WWW. With billions of webpages in existence today, it is important to develop efficient means of locating the relevant webpages on a given topic. A single topic may have thousands of relevant pages of varying popularity. Top – k document retrieval systems identifies the top – k ranked webpages pertaining to a given topic. In this paper, we propose an efficient top-k document retrieval method (TkRSAGA), that works on the existing search engines using the combination of Simulated Annealing and Genetic Algorithms. The Simulated Annealing is used as an optimized search technique in locating the top-k relevant webpages, while Genetic Algorithms helps in faster convergence via parallelism. Simulations were conducted on real datasets and the results indicate that TkRSAGA outperforms the existing algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Yao, X.: Simulated Annealing with Extended Neighbourhood. International Journal of Computer Mathematics 40, 169–189 (1991)
Yao, X.: Optimization by Genetic Annealing. In: Proc. Second Australian Conference on Neural Networks, pp. 94–97 (1991)
Szu, H.H., Hartley, R.L.: Fast Simulated Annealing. Physics Letters 122, 157–162 (1982)
Ingber, L.: Very Fast Simulated Re-Annealing. Mathl. Comput. Modelling 12(8), 967–973 (1989)
Kleinberg, J.M.: Authoritative Sources in a Hyperlinked Environment. In: Proc. ACM - SIAM Symp. on Discrete Algorithms (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shenoy, P.D., Srinivasa, K.G., Thomas, A.O., Venugopal, K.R., Patnaik, L.M. (2004). Mining Top – k Ranked Webpages Using Simulated Annealing and Genetic Algorithms. In: Manandhar, S., Austin, J., Desai, U., Oyanagi, Y., Talukder, A.K. (eds) Applied Computing. AACC 2004. Lecture Notes in Computer Science, vol 3285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30176-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-30176-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23659-7
Online ISBN: 978-3-540-30176-9
eBook Packages: Springer Book Archive