Iterated mutation in an evolutionary algorithm for Sudoku

Authors:
Donald O. Hamnes;Bryant A. Julstrom
Affiliations:
St. Cloud State University, St. Cloud, MN;St. Cloud State University, St. Cloud, MN
Venue:
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Year:
2008

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Abstract

A Sudoku puzzle specifies symbols from the set S = {1, 2, ..., 9} for some cells in a 9x9 grid, with no duplicate symbols in any of the grid's rows, columns, or non-overlapping 3x3 regions. The challenge is to fill the grid's remaining cells with symbols from S in such a way that the non-duplicate condition is preserved. An evolutionary algorithm to solve Sudoku puzzles encodes candidate solutions as 9x9 arrays. To these chromosomes, the EA applies operators based on the significant sub-structures of a puzzle: its rows, columns, and non-overlapping 3x3 regions. These operations include crossover, mutation, and an iterated mutation that we call wandering. Repeated trials of the EA on 100 published Sudoku puzzles all quickly found solutions.