Optimised Random Mutations for Evolutionary Algorithms

Abstract

To demonstrate our approaches we will use Sudoku puzzles, which are an excellent test bed for evolutionary algorithms. The puzzles are accessible enough for people to enjoy. However the more complex puzzles require thousands of iterations before an evolutionary algorithm finds a solution. If we were attempting to compare evolutionary algorithms we could count their iterations to solution as an indicator of relative efficiency. Evolutionary algorithms however include a process of random mutation for solution candidates. We will show that by improving the random mutation behaviours we were 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.