|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Title:||Mining Markov Network Surrogates to Explain the Results of Metaheuristic Optimisation|
|Citation:||Brownlee A, Wallace A & Cairns D (2021) Mining Markov Network Surrogates to Explain the Results of Metaheuristic Optimisation. In: Martin K, Wiratunga N & Wijekoon A (eds.) Proceedings of the SICSA eXplainable Artifical Intelligence Workshop 2021. CEUR Workshop Proceedings, 2894. SICSA eXplainable Artifical Intelligence Workshop 2021, Aberdeen, 01.06.2021-01.06.2021. Aachen: CEUR Workshop Proceedings, pp. 64-70. http://ceur-ws.org/Vol-2894/short9.pdf|
|Series/Report no.:||CEUR Workshop Proceedings, 2894|
|Conference Name:||SICSA eXplainable Artifical Intelligence Workshop 2021|
|Conference Dates:||2021-06-01 - 2021-06-01|
|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.|
|Status:||VoR - Version of Record|
|Rights:||Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0 - https://creativecommons.org/licenses/by/4.0/).|
|short9.pdf||Fulltext - Published Version||710.64 kB||Adobe PDF||View/Open|
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