Research Output
Automated, Explainable Rule Extraction from MAP-Elites archives
  Quality-diversity(QD) algorithms that return a large archive of elite solutions to a problem provide insights into how high-performing solutions are distributed throughout a feature-space defined by a user — they are often described as illuminating the feature-space, providing a qualitative illustration of relationships between features and objective quality. However, if there are 1000s of solutions in an archive, extracting a succinct set of rules that capture these relationships in a quantitative manner (i.e. as a set of rules) is challenging. We propose two methods for the automated generation of rules from data contained in an archive; the first uses Genetic Programming and the second, a rule-induction method known as CN2. Rules are generated from large archives of data produced by running MAP-Elites on an urban logistics problem. A quantitative and qualitative evaluation that includes the end-user demonstrate that the rules are capable of fitting the data, but also highlights some mis- matches between the model used by the optimiser and that assumed by the user.


Urquhart, N., Höhl, S., & Hart, E. (2021). Automated, Explainable Rule Extraction from MAP-Elites archives. In Applications of Evolutionary Computation: 24th International Conference, EvoApplications 2021. , (258-272).



Real-World, Logistics, Optimisation

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