Interpretable Decision Trees to Predict Solution Fitness
G. Catalano, Alexander E. I. Brownlee, David R. Cairns, Russell Ainslie, John McCall · Proceedings of the Genetic and Evolutionary Computation Conference · 2025
Metaheuristic algorithms are powerful tools for tackling complex optimization problems, but their black-box nature often hinders user trust and understanding. This paper presents a novel methodology for enhancing the explainability of metaheuristics by employing decision trees with splitting criteria based on Partial Solutions. These represent beneficial sub-structures of solutions and provide insights into the problem landscape and solution characteristics. By constructing decision trees that consider the presence or absence of specific patterns in solutions, we produce a transparent model capable of predicting solution fitness.