Sean McGerty and Frank Moisiadis, University of Notre Dame, Australia

Sudoku puzzles are an excellent testbed for evolutionary algorithms. The puzzles are accessible enough to be enjoyed by people. However the more complex puzzles require thousands of iterations before a solution is found by an evolutionary algorithm. If we were attempting to compare evolutionary algorithms we could count their iterations to solution as a indicator of relative efficiency. However all evolutionary algorithms include a process of random mutation for solution candidates. I will show that by improving the random mutation behaviours I was able to solve problems with minimal evolutionary optimisation. Experiments demonstrated the random mutation was at times more effective at solving the harder problems than the evolutionary algorithms. This implies that the quality of random mutation may have a significant impact on the performance of evolutionary algorithms with sudoku puzzles. Additionally this random mutation may hold promise for reuse in hybrid evolutionary algorithm behaviours.

attention, adaption, artificial intelligence, evolution, exploitation, exploration, satisficing, sudoku, particle swarm, genetic algorithm, simulated annealing, mutation.