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Interactive Genetic Algorithms Based on Implicit Knowledge Model

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Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

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Abstract

Interactive genetic algorithms depend on more knowledge embodied in evolution than other genetic algorithms for explicit fitness functions. But there is a lack of systemic analysis about implicit knowledge of interactive genetic algorithms. Aiming at above problems, an interactive genetic algorithm based on implicit knowledge model is proposed. The knowledge model consisting of users’ cognition tendency and the degree of users’ preference is put forward, which describes implicit knowledge about users’ cognitive and preference. Based on the concept of information entropy, a series of novel operators to realize extracting, updating and utilizing knowledge are illustrated. To analyze the performance of knowledge-based interactive genetic algorithms, two novel measures of dynamic stability and the degree of users’ fatigue are presented. Taking fashion design system as a test platform, the rationality of knowledge model and the effective of knowledge induced strategy are proved. Simulation results indicate this algorithm can alleviate users’ fatigue and improve the speed of convergence effectively.

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Author information

Authors and Affiliations

  1. School of Information and Electronic Engineering, China University of Mining, and Technology, 221008, Xuzhou, Jiangsu, China

    Yi-nan Guo, Dun-wei Gong & Ding-quan Yang

Authors
  1. Yi-nan Guo
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  2. Dun-wei Gong
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  3. Ding-quan Yang
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Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Guo, Yn., Gong, Dw., Yang, Dq. (2006). Interactive Genetic Algorithms Based on Implicit Knowledge Model. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_47

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  • DOI: https://doi.org/10.1007/11903697_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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