Solving Full NxNxN Rubik's Supercube Using Genetic Algorithm

被引:0
|
作者
Swita, Robert [1 ]
Suszynski, Zbigniew [1 ]
机构
[1] Koszalin Univ Technol, Fac Elect & Informat, Koszalin, Poland
关键词
CUBE;
D O I
10.1155/2023/2445335
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The article presents an algorithm that uses an evolutionary approach to the problem of solving the Full Rubik NxNxN Supercube, i.e., the orientation of all cubies, including the internal ones, not only according to face colors but to the same orientation in 3D space. The problem is formally defined by the matrix representation using affine cubies transforms. The Full Supercube's solving strategy uses a series of genetic algorithms that try to find a better cube configuration than the current one. Once found, movements are made to change the current configuration. This strategy is repeated until the cube is solved. The genetic algorithm limits the movements to the current cluster by solving the cube in stages, outwards from the center of the cube. The movements that solve the clusters are saved as macros and used to train and speed up the algorithm. The purpose of the presented algorithm is to minimize the solution time and not necessarily the number of moves.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Solving Deceptive Problems Using A Genetic Algorithm with Reserve Selection
    Chen, Yang
    Hu, Jinglu
    Hirasawa, Kotaro
    Yu, Songnian
    2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8, 2008, : 884 - +
  • [42] Solving composite scheduling problems using the hybrid genetic algorithm
    Azuma OKAMOTO
    Mitsumasa SUGAWARA
    Journal of Zhejiang University-Science A(Applied Physics & Engineering), 2010, 11 (12) : 953 - 958
  • [43] Solving an assembly sequence optimisation problem using the genetic algorithm
    Alharbi, Fawaz
    Wang, Qian
    2018 INTERNATIONAL CONFERENCE ON ELECTRONICS, CONTROL, OPTIMIZATION AND COMPUTER SCIENCE (ICECOCS), 2018,
  • [44] Solving expert assignment problem using improved genetic algorithm
    Li, Na-Na
    Zhang, Jian-Nan
    Gu, Jun-Hua
    Liu, Bo-Ying
    PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2007, : 934 - +
  • [45] Solving the graph planarization problem using an improved genetic algorithm
    Wang, Rong-Long
    Okazaki, Kozo
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2006, E89A (05) : 1507 - 1512
  • [46] Solving Asymmetric Traveling Salesman Problem using Genetic Algorithm
    Birtane Akar, Sibel
    Sahingoz, Ozgur Koray
    2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2015, : 1655 - 1659
  • [47] Solving composite scheduling problems using the hybrid genetic algorithm
    Azuma OKAMOTO
    Mitsumasa SUGAWARA
    Journal of Zhejiang University-Science A(Applied Physics & Engineering), 2010, (12) : 953 - 958
  • [48] Solving the Container Relocation Problem by Using a Metaheuristic Genetic Algorithm
    Gulic, Marko
    Maglic, Livia
    Krljan, Tomislav
    Maglic, Lovro
    APPLIED SCIENCES-BASEL, 2022, 12 (15):
  • [49] Solving Rubik's cube via quantum mechanics and deep reinforcement learning
    Corli, Sebastiano
    Moro, Lorenzo
    Galli, Davide E.
    Prati, Enrico
    JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2021, 54 (42)
  • [50] Phospholipid hydrolysis and insulin secretion: a step toward solving the Rubik's cube
    Poitout, Vincent
    AMERICAN JOURNAL OF PHYSIOLOGY-ENDOCRINOLOGY AND METABOLISM, 2008, 294 (02): : E214 - E216