Merging Neural Networks with Traditional Evaluations in Crazyhouse

In the intricate landscape of game-playing algorithms, Crazyhouse stands as a complex variant of chess where captured pieces are reintroduced, presenting unique evaluation challenges. This paper explores a hybrid approach that combines traditional evaluation functions with neural network-based evaluations, seeking an optimal balance in performance. Through rigorous experimentation, including self-play, matchups against a variant of the renowned program, Go-deep experiments, and score deviations, we present compelling evidence for the effectiveness of a weighted sum of both evaluations. Remarkably, in our experiments, the combination of 75% neural network and 25% traditional evaluation consistently emerged as the most effective choice. Furthermore, we introduce the use of Best-Change rates, which have previously been associated with evaluation quality, in the context of Monte Carlo tree search-based algorithms.