Mining Markov Network Surrogates to Explain the Results of Metaheuristic Optimisation

Abstract

Metaheuristics are randomised search algorithms that are effective at finding ”good enough” solutions to optimisation problems. However, they present no justification for the generated solutions, and are non-trivial to analyse. We propose that identifying which combinations of variables strongly influence solution quality, and the nature of that relationship, represents a step towards explaining the choices made by the algorithm. Here, we present an approach to mining this information from a “surrogate fitness function” within a metaheuristic. The approach is demonstrated with two simple examples and a real-world case study.