An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems
Journal article, Peer reviewed
“ n o t i c e: this is the author’s version of a work that was accepted for publication in expert systems with applications. changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. changes may have been made to this work since it was submitted for publication. a definitive version was subsequently published in expert systems with applications, [38, 4, april 2011] http://dx.doi.org/10.1016/j.eswa.2010.09.041”
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Original versionHuang, Y-P., Chang, Y-T., Hsieh, S-L. & Sandnes, F.E. (2011). An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems. Expert Systems with Applications, 38 (4), 3806-3818 http://dx.doi.org/10.1016/j.eswa.2010.09.041
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.