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An Evolutionary Computing Solution to the Jump It Problem

Published:25 May 2020Publication History

ABSTRACT

In this paper, we present an evolutionary computing solution to the Jump-It game problem, which is a board playing optimization problem. We compare the evolutionary computing solution with a dynamic programming solution to the problem. We also report on how we used the Jump-It problem to introduce evolutionary computing to undergraduate students in a data mining course.

References

  1. C. Ahn and R. Ramakrishna, Elitism-based Compact Genetic Algorithms, in IEEE Transactions on Evolutionary Computation, 2003.Google ScholarGoogle Scholar
  2. A. Baicoianu. Implementing Smart Applications Using Genetic Algorithms, Bulletin of the Transilvania University of Brasov, 10 (2), 143--154, 2017.Google ScholarGoogle Scholar
  3. A. Eiben and J. Smith. Introduction to Evolutionary Computing, 2nd ed., Berlin: Springer, 2015.Google ScholarGoogle Scholar
  4. D. Larose and C. Larose. Data Mining and Predictive Analytics, 2nd ed., New Jersey, Hoboken: Wiley, 2015.Google ScholarGoogle Scholar
  5. Q. Lu, S. Li, W. Zhang, and L. Zhang. A Genetic Algorithm-based Job Scheduling Model for Big Data Analytics, EURASIP Journal on Wireless Communications & Networking, 2016 (1), 1--9, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  6. M. Rocha, P. Corte, and J. Neves. Evolution of Neural Networks for Classification and Regression, Neurocomputing, 70 (16-18), 2809--2816, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. Saquer and R. Iqbal. 2017. 2-in-1 with the Jump-It Game. Journal of Computing Sciences in Colleges, Volume 33, Issue 1, October 2017.Google ScholarGoogle Scholar
  8. K. Srinivasan, M. Subramanian, and B. Bhagavathsingh. Optimized Bilevel Classifier for Brain Tumor Type and Grade Discrimination Using Evolutionary Fuzzy Computing, Turkish Journal of Electrical Engineering & Computer Sciences. 27 (3), 1704-1 2019.Google ScholarGoogle ScholarCross RefCross Ref
  9. J. Zhang, F., Yang, and X. Weng. A Building-Block-Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System, Mathematical Problems in Engineering, 2019, 1--15.Google ScholarGoogle Scholar

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