An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems

  • Authors:

  • Yo-Ping Huang;Yueh-Tsun Chang;Shang-Lin Hsieh;Frode Eika Sandnes

  • Affiliations:

  • Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan;Department of Computer Science and Engineering, Tatung University, Taipei 10451, Taiwan;Department of Computer Science and Engineering, Tatung University, Taipei 10451, Taiwan;Faculty of Engineering, Oslo University College, Oslo, Norway

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

Most real-world problems cannot be mathematically defined and/or structured modularly for 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 others. In order to apply someone's research results to new domains and for researchers to collaborate with each other more efficiently, a well-defined architecture with self-adaptive evolution strategies is proposed. It can automatically find the best solutions from existing knowledge and previous research experiences. The proposed architecture is based on object-oriented programming skills that in turn become foundations of the community interaction evolution strategy and knowledge sharing mechanism. They make up an autonomous evolution mechanism using a progressive 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 tuning of the problems, optimal or near-optimal solutions are feasible.