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Benchmarking Rubik’sRevenge algorithms
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This Bachelor thesis paper investigates 2 different methods used to solve the Rubik’s Cube

4x4x4 puzzle. The analyzed methods are Reduction and Big Cube method. We have

implemented the cube and the two solvers in Python. Through a series of tests we have

concluded that the Big Cube method has a better average move count as well as a low standard

deviation in comparison to the Reduction method. However the reduction method has a lower

minimum move count and consists of fewer algorithms. The best approach would be to combine

both methods to form an optimal solution.

Abstract [sv]

Denna kandidatexamensuppsats undersöker två olika metoder som används för att lösa Rubiks

Kub 4x4x4. Metoderna som analyseras är Reduction och Big Cube. Vi har implementerat kuben

samt de bägge lösarna I Python. Genom en serie tester har vi kommit fram till att Big Cube har

ett lägre genomsnittligt rotationsantal samt lägre standardavvikelse än Reduction. Reductionmetoden

har däremot ett lägre minimumvärde på antalet rotationer och består av färre

algoritmer. Det bästa tillvägagångssättet vore att kombinera de båda lösningarna.

Place, publisher, year, edition, pages
Kandidatexjobb CSC, K13016
National Category
Computer Science
URN: urn:nbn:se:kth:diva-134903OAI: diva2:668678
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2013-12-13 Created: 2013-12-02 Last updated: 2013-12-13Bibliographically approved

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