Abstract
Sudoku puzzles are easily solved using backtracking algorithms. Yet,
literature is scattered with various shy and often opaque attempts at
using evolutionary algorithms and hybridizing them with known strategies
of solving the puzzle. Evolutionary methods are serendipitous in nature,
and this paper demonstrates the behaviour of such serendipity under
constraints, using visuals that depict the sheer magnitude of the
problem space and the nature in which intertwined constraints affect the
scope for locating a solution, with the hope that it could inspire a new
way of looking at the problem. We propose a method of visualizing the
sudoku fitness landscape, the vastness and complexity of even partially
brute forcing the puzzle, and a unique method of mutating puzzle states
using circular swaps. These insights could potentially serve as a link
to comprehend the problem space when designing solutions for vast,
multidimensional problems. Additionally, finding the optimal solution
for some puzzles was notably harder, compared to puzzles in the same
category of given clues. A short investigation was conducted into this
phenomenon, which revealed hints that compel us to propose that the
direction of research that should be taken, is in discovering more about
puzzle states and definitive mathematical properties of the puzzle,
rather than merely designing brute-force, stochastic or hybrid
approaches of finding solutions.
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