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Solving Sudoku Using Neighbourhood-Based Mutation Approach of Genetic Algorithm
S. Jana, A. Dey, A.K. Maji,
Published in Springer Science and Business Media Deutschland GmbH
Volume: 241
Pages: 153 - 167
Sudoku, an NP-complete-based mathematical puzzle, has different levels of difficulty. There are several techniques to solve this interesting mathematical conundrum. Our effort anticipated a neighbourhood-based mutation approach of the Genetic Algorithm to solve Sudoku instances. In this paper, the fixed two-point crossover along with neighbourhood-based mutation is implemented. For mutation, a neighbour checking concept is incorporated to get rid of unwanted swaps. The newness of our proposed method is that considering less population Sudoku instances can be solved with a greater success rate for easy, medium, and hard difficulty level puzzles. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About the journal
JournalData powered by TypesetLecture Notes in Networks and Systems
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH