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Title page for etd-0902109-030343


URN etd-0902109-030343 Statistics This thesis had been viewed 1762 times. Download 15 times.
Author Yueh-Tsun Chang
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2008 Semester 2
Degree Ph.D. Type of Document Doctoral Dissertation
Language Chinese&English Page Count 125
Title An Architecture and Strategy for Self-Adaptive Community Knowledge Evolution
Keyword
  • Community Interaction
  • Self Adaptation
  • Evolution Computation
  • Knowledge Sharing
  • Learning Strategy
  • Learning Strategy
  • Knowledge Sharing
  • Evolution Computation
  • Self Adaptation
  • Community Interaction
  • Abstract Most real-world problems cannot be mathematically defined and/or structured modularly to enable peer researchers in the same community to facilitate their work. This is partially because there are no concrete defined methods that can help researchers clearly describe their problems and partially because one method fits one problem but does not apply to the others. These are some of the reasons that retard researchers in the same community the information exchange and knowledge sharing.
    In order to apply someone’s research results to new domains and for researchers to collaborate with each other more conveniently, a well-defined architecture with self-adaptive evolution strategies is proposed. It can automatically find the best solutions from available knowledge and previous research experiences. The proposed architecture is based on object-orient programming skills that in turn become foundations of community interaction evolution strategy and knowledge sharing mechanism. They make up an autonomous evolution mechanism using progressively learning strategy and a common knowledge packaging definition. The architecture defines fourteen highly modular classes that allow users to enhance collaboration with others in the same or similar research community. The presented evolution strategies also integrate the merits of users’ predefined algorithms, group interaction and learning theory to approach the best solutions of specific problems.
    Finally, resource limitation problems are tackled to verify both the re-usability and flexibility of the proposed work. Our results show that even without using any specific configuration to the problems, optimal or near-optimal solutions are also feasible.
    Advisor Committee
  • Shang-Lin Hsieh - advisor
  • Yo-Ping Huang - advisor
  • none - co-chair
  • none - co-chair
  • none - co-chair
  • none - co-chair
  • Files indicate in-campus access only
    Date of Defense 2009-07-29 Date of Submission 2009-09-02


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