An Adaptive Algorithm of Showing Intuitively the Structure of a Binary Tree

  • Pin-Ju YeEmail author
  • Chun-Guo Huang
  • Yuan-Wang Hu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 834)


The adaptive software development(ASD) method is an agile development method based on complex adaptive system theory. It develops adaptive abilities through methods such as candidate program library, dynamic display selection, semantic self-description and self-test, and establishes expert system through the study of business rules, achieves intelligent adaptable software by decision theory, diagnosis, recovery and other methods. This paper presents an adaptive algorithm of showing intuitively the structure of a binary tree, the algorithm module is plug and play, and can be inserted directly into any program source code related to the binary tree without any modification. This technology can improve the efficiency of system design, reduce the workload of program maintenance and has some innovation and practicability.


Binary tree Adaptive Semantic Self-description Plug and play introduction 



This work is supported by Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) under Grant No. PPZY2015A090.


  1. 1.
    Cohen, J., Rodrigues, L.A., Duarte, E.P.: Parallel cut tree algorithms. J. Parallel Distrib. Comput. 109(11), 1–14 (2017)CrossRefGoogle Scholar
  2. 2.
    Wang, Q., Liu Z.: A cost-sensitive decision tree optimized algorithm based on adaptive mechanism. In: 2017 3rd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2017), pp. 170–175. DEStech Publications (2017)Google Scholar
  3. 3.
    Li, J., Wang, Y.: A new fast reduction technique based on binary nearest neighbor tree. Orig. Res. Artic. Neurocomputing 149(2), 1647–1657 (2015)CrossRefGoogle Scholar
  4. 4.
    Maßberg, J.: Generalized Huffman coding for binary trees with choosable edge lengths. Inf. Process. Lett. 115(4), 502–506 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Xiaofang, J., Mengxuan, L., Min, S., Libiao, J., Xianglin, H.: Research on the adaptive hybrid search tree anti-collision algorithm in RFID system. High Technol. Lett. 22(1), 107–112 (2016)Google Scholar
  6. 6.
    Jinguang, S., Suhong, W.: Collision Detection optimization algorithm based on classified traversal. J. Comput. Appl. 35(1), 194–197 (2015)Google Scholar
  7. 7.
    Shu-qin, H., Hai Z.: Research on deleting node algorithm of binary sort tree. J. Tonghua Norm. Univ. 12, 46–48 (2014)Google Scholar
  8. 8.
    Wen-yi, L., Ming-dao, Y., Xian-zhao, L., Jun, H.: Optimal evidence selection method using binary tree. J. Anyang Inst. Technol. Struct. 13(3), 50–53 (2014)Google Scholar
  9. 9.
    Haraburda, D., Tarau, P.: Binary trees as a computational framework. Orig. Res. Artic. Comput. Lang. Syst. Struct. 39(4), 163–181 (2013)zbMATHGoogle Scholar
  10. 10.
    Jing-shan M.A., Yu-ping, Q.: Level traversal binary tree in sequence storage and its application. J. Bohai Univ. Sci. (Natural Science Edition) 2, 172–176 (2013)Google Scholar
  11. 11.
    Zulhisam, M., Riaza, R.: Learning binary tree algorithm using 3-d visualization: an early results. Orig. Res. Artic. Procedia Soc. Behav. Sci. 90(10), 388–395 (2013)CrossRefGoogle Scholar
  12. 12.
    Xing-bo, W.: Study on non-recursive and stack-free algorithms for preorder traversal of complete binary trees. Comput. Eng. Des. 9, 3078–3081 (2011)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of SoftwareChangzhou College of Information TechnologyChangzhouChina
  2. 2.School of Computer EngineeringJiangsu University of TechnologyChangzhouChina
  3. 3.School of Electronic and Electrical EngineeringChangzhou College of Information TechnologyChangzhouChina

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