Mining Potentially Explanatory Patterns via Partial Solutions
G. Catalano, Alexander E. I. Brownlee, David R. Cairns, John McCall, Russell Ainslie · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2024
We introduce Partial Solutions to improve the explainability of genetic algorithms for combinatorial optimization. Partial Solutions represent beneficial traits found by analyzing a population, and are presented to the user for explainability, but also provide an explicit model from which new solutions can be generated. We present an algorithm that assembles a collection of explanatory Partial Solutions chosen to strike a balance between simplicity, high fitness and atomicity, that are shown to be able to solve standard optimization benchmarks.