Automated, Explainable Rule Extraction from MAP-Elites archives

Urquhart, Neil; Höhl, Silke; Hart, Emma

Abstract

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.

Citation

Urquhart, N., Höhl, S., & Hart, E. (2021, April). Automated, Explainable Rule Extraction from MAP-Elites archives. Presented at EvoAPPs2021, Online

• Presentation Conference Type: Conference Paper (published)
• Conference Name: EvoAPPs2021
• Start Date: Apr 7, 2021
• End Date: Apr 9, 2021
• Acceptance Date: Jan 20, 2021
• Online Publication Date: Apr 1, 2021
• Publication Date: 2021-04
• Deposit Date: Jan 25, 2021
• Publicly Available Date: Apr 2, 2022
• Publisher: Springer
• Pages: 258-272
• Series Title: Lecture Notes in Computer Science
• Series Number: 12694
• Series ISSN: 1611-3349
• Book Title: Applications of Evolutionary Computation: 24th International Conference, EvoApplications 2021
• ISBN: 978-3-030-72698-0
• DOI: https://doi.org/10.1007/978-3-030-72699-7_17
• Keywords: Real-World, Logistics, Optimisation
• Public URL: http://researchrepository.napier.ac.uk/Output/2717414