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The Optimal Solution of TSP Using the New Mixture Initialization and Sequential Transformation Method in Genetic Algorithm

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PRICAI 2006: Trends in Artificial Intelligence (PRICAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4099))

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Abstract

TSP is a problem finding out the shortest distance out of possible courses where one starts a certain city and turns back to a starting city, visiting every city only once among N cities. This paper proposes the new method using both population initialization and sequential transformation method at the same time and then proves the improvement of capability by comparing them with existing methods.

This study was supported by research funds from Chosun University, 2006.

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References

  1. Gou, J.G.: Genetic Algorithm and the application, kousa (2000)

    Google Scholar 

  2. Goldberg, D.: Genetic Algorithms in search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  3. Boese, K.D.: Cost Versus Distanc. In: the Traveling Salesman Problem, Technical Report CSD-950018, UCLA (1995)

    Google Scholar 

  4. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)

    MATH  Google Scholar 

  5. Grefenstette, J., Gopal, R., Rosmaita, B., Gucht, D.: Genetic Algorithms for the Traveling Salesman Problem. In: Proc. the 1st Inter. Conf. on GAs and Their Applications, pp. 160–168 (1985)

    Google Scholar 

  6. Whitley, D., Starkweather, T., Fuquay, D.: Scheduling problems and traveling salesman: the genetic edge recombination and operator. In: Proc. Third Int. Conf. G As, pp. 133–140 (1989)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. Computer Science & Statistic, Chosun University, 375 Seoseok-dong Dong-gu, Gwangju, Korea

    Rae-Goo Kang & Chai-Yeoung Jung

Authors
  1. Rae-Goo Kang
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  2. Chai-Yeoung Jung
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Editor information

Editors and Affiliations

  1. The Hong Kong University of Science and Technology,, Hong Kong

    Qiang Yang

  2. Clayton School of Information Technology, Monash University, P.O. Box, Australia

    Geoff Webb

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© 2006 Springer-Verlag Berlin Heidelberg

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Kang, RG., Jung, CY. (2006). The Optimal Solution of TSP Using the New Mixture Initialization and Sequential Transformation Method in Genetic Algorithm. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_157

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  • DOI: https://doi.org/10.1007/978-3-540-36668-3_157

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36667-6

  • Online ISBN: 978-3-540-36668-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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