Evaluating a Parallel Evolutionary Algorithm on the Chess Endgame Problem

Classifying the endgame positions in Chess can be challenging for humans and is known to be a difficult task in machine learning. An evolutionary algorithm would seem to be the ideal choice. We describe our implementation of a parallel island model and evaluate it in the context of the Chess Endgame data set from the UCI machine learning repository. We are mainly interested in impact of parallelization upon runtime and accuracy. Thus, we compare the system’s performance under a number of varied conditions, including population size, number of islands, number of neighbors, migration rate, and migrant selection strategy. These results show the system to be useful from an efficiency standpoint, and point to opportunities to better understand the behavior and properties of the islandmodel evolutionary algorithm.