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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Brownlee, Alexander
Wallace, Aidan
Cairns, David
Title: Mining Markov Network Surrogates to Explain the Results of Metaheuristic Optimisation
Editor(s): Martin, Kyle
Wiratunga, Nirmalie
Wijekoon, Anjana
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.
Issue Date: 2021
Date Deposited: 18-Oct-2021
Series/Report no.: CEUR Workshop Proceedings, 2894
Conference Name: SICSA eXplainable Artifical Intelligence Workshop 2021
Conference Dates: 2021-06-01 - 2021-06-01
Conference Location: Aberdeen
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 -
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