At international cat shows cats must be assigned to judges for evaluation. Many criteria must be considered in preparation of such distributions, and there can be several hundred cats signed in. The difficulty of preparing a good distribution therefore exceeds human capacity so we use the methods of artificial intelligence to optimize the distribution of cats among the judges.
We present some algorithms that can be used for creating judging distributions at international cat shows. First we defined a fitness function that assigns to a distribution a numerical value that represents its quality. Then, we implemented several algorithms for finding an optimal distribution: exhaustive search, random search, several variants of greedy search, Monte Carlo tree search and local search. We obtained the judging distributions of some previous cat shows and evaluated them using our fitness function. We prepared optimized distributions using the above algorithms and compared them with actually used distributions. The comparison proves that the optimized judging distributions, although not ideal, are significantly better than currently used judging distributions prepared by experts.
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