E.Coli Search: Self Replicating Agents for Web Based Information Retrieval

  • Derrick Takeshi Mirikitani
  • Ibrahim Kushchu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2690)


Although search engines are often used for information retrieval (IR) from the World Wide Web (WWW), current search engine technology seems obsolete. The quality of query results from today’s search engines is unacceptable, creating a demand for new information search and retrieval techniques. The conventional IR methods often lack the flexibility to adapt to changes in the content of the WWW. This paper presents an overview of new developments in evolutionary and adaptive IR and proposes a system (E.Coli search) where an adaptive population of intelligent agents forage the web in search of relevant documents.


Information Retrieval evolutionary adaptive agents World Wide Web 


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  1. 1.
    Aggarwal, C.C., Al-Garawi, F., Yu, P.S.: Intelligent Crawling on the World Wide Web with Arbitrary Predicates. In: WWW10 2001. ACM, Hong Kong (2001)Google Scholar
  2. 2.
    Albert, Jeong, Barabasi: Diameter of the World Wide Web. Nature 401(130) (1999)Google Scholar
  3. 3.
    Berry, M.W., Browne, M.: Understanding Search Engines Mathematical Modeling and Text Retrieval Society for Industrial and Applied Mathematics, Philadelphia (1999)Google Scholar
  4. 4.
    Blew, R.K.: Finding out About: a cognitive perspective on search engine technology. Cambridge University Press, Cambridge (2000)Google Scholar
  5. 5.
    Blockeel, H., Kosala, R.: Web Mining Research: A Survey SIGKDD Explorations. ACM SIGKDD (2000)Google Scholar
  6. 6.
    Bunde, A., Havlin, S. (eds.): Fractals in Science. Springer, Berlin (1994)zbMATHGoogle Scholar
  7. 7.
    Chen, H., Chung, Y., Ramsey, M., Yang, C.: An intelligent personal spider (agent) for dynamic internet/intranet searching (1998)Google Scholar
  8. 8.
    De Bra, P.: Post Information retrieval in the world wide web: Making client-based searching feasible. In: Is’ International WWW Conference, Geneva (1994)Google Scholar
  9. 9.
    Kuscu, I.: An Adaptive Approach to Organizational Knowledge Management. Knowledge and Information: Journal of the KMCI 1(2) (2001)Google Scholar
  10. 10.
    Kim, Y.H., Kim, S., Eom, J.H., Zhang, B.-T.: Scai experiments on trec-9. In: Proceedings of the Ninth Text REtrieval Conference {TREC-9), pp. 392–399 (2000)Google Scholar
  11. 11.
    Kim, S., Zhang, B.-T.: Evolutionary learning of web document structure for information retrieval. In: Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, May 27-30, pp. 1253–1260. IEEE Press, Los Alamitos (2001)Google Scholar
  12. 12.
    Pant, G., Menczer, F.: Myspiders: Evolve your own intelligent web crawlers. Autonomous Agents and Multi-Agent Systems 5(2), 221–229 (2002)CrossRefGoogle Scholar
  13. 13.
    Langton, C.G.: Artificial Life: The Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems. Addison-Wesley, Redwood City (1989)Google Scholar
  14. 14.
    Lawrence, S., Giles, C.L.: Accessibility and Distribution of Information on the Web. Nature 400(6740), 107–109 (1999)CrossRefGoogle Scholar
  15. 15.
    Levy, S.: Artificial Life The Quest for a New Creation Jonathan Cape, London (1992)Google Scholar
  16. 16.
    Maes, P.: Agents that Reduce Work and Information Overload. Communications of the ACM 37(7) (July 1994)Google Scholar
  17. 17.
    Menczer, F.: Complementing Search Engines with Online Web Mining Agents. Science. Elsevier, Amsterdam (2002)Google Scholar
  18. 18.
    Menczer, F., Richard, K.: Blew Adaptive Information Agents in Distributed Textual Environments. In: Agents 1998: Proceedings of the Second International Conference on Autonomous Agents, Minneapolis, MN. ACM, New York (1998)Google Scholar
  19. 19.
    Menczer, F., Richard, K.: Adaptive retrieval agents: Internalizing local context and scaling up to the web. Machine Learning 39(2/3), 203–242 (2000)zbMATHCrossRefGoogle Scholar
  20. 20.
    Menczer, F., Richard, K.: Belew, W. Willuhn. Artificial life applied to adaptive information agents. In: AAAI Spring Symposium on Information Gathering (1995)Google Scholar
  21. 21.
    Moukas, A.: Amalthaea: Information discovery and filtering using a multiagent evolving ecosystem. In: Proceedings of the Conference on Practical Applications of Agents and Multiagent Technology (1996)Google Scholar
  22. 22.
    Moukas, A., Maes, P.: Amalthaea: An evolving multi-agent information filtering and discovery system for the WWW. Autonomous Agents and Multi-Agent Systems l(l), 59–88 (1998)CrossRefGoogle Scholar
  23. 23.
    Oh, J.C.: Cooperating search agents explore more than defecting search agents in the internet information access. In: Proceedings of the 2001 Congress on Evolutionary Computation CEC200I, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, May 27-30, pp. 1261–1268. IEEE Press, Los Alamitos (2001)Google Scholar
  24. 24.
    Pagliarini, L., Dolan, A., Menczer, F., Lund, H.H.: A Life meets web: Lessons learned. In: Virtual Worlds, pp. 156-167 (1998)Google Scholar
  25. 25.
    Pathak, P., Gordon, M., Fan, W.: Effective information retrieval using genetic algorithms based matching functions adaptation. In: HICSS (2000)Google Scholar
  26. 26.
    Pereira, F., Costa, E.: How learning improves the performance of evolutionary agents: a case study with an information retrieval system for a distributed environmentGoogle Scholar
  27. 27.
    Pereira, F.B., Costa, E.: How adaptive agents learn to deal with incomplete queries in distributed information environments. In: Proc. of the 2000 Congress on Evolutionary Computation, pp. 1329–1336. IEEE Computer Society Press, Los Alamitos (2000)Google Scholar
  28. 28.
    Walker, R.L.: Assessment of the web using genetic programming. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, July 13-17, vol. 2, pp. 1750–1755. Morgan Kaufmann, San Francisco (1999)Google Scholar
  29. 29.
    Walker, R.L.: Dynamic load balancing model: Preliminary assessment of a biological model for a pseudo-search engine. In: SPDP: IEEE Symposium on Parallel and Distributed Processing. ACM Special Interest Group on Computer Architecture (S1GARCH). IEEE Computer Society, Los Alamitos (2000)Google Scholar
  30. 30.
    Walker, R.L.: Parallel clustering system using the methodologies of evolutionary computations. In: Proceedings of the 2001 Congress on Evolutionary Computation CEC 200I, COEX, World Trade Center, 159 Samseongdong, Gangnam-gu, Seoul, Korea, May 27-30, pp. 831-938. IEEE Press, Los Alamitos ( 2001)Google Scholar
  31. 31.
    Walker, R.L.: Search engine case study searching the web using genetic programming. In: Parallel Computing, vol. 7, Elsevier, Amsterdam (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Derrick Takeshi Mirikitani
    • 1
  • Ibrahim Kushchu
    • 1
  1. 1.GSIMInternational University of JapanNiigataJAPAN

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