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Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms.

Fyvie, Martin; McCall, John A.W.; Christie, Lee A.

Authors



Contributors

Max Bramer
Editor

Richard Ellis
Editor

Abstract

The generation of explanations regarding decisions made by population-based meta-heuristics is often a difficult task due to the nature of the mechanisms employed by these approaches. With the increase in use of these methods for optimisation in industries that require end-user confirmation, the need for explanations has also grown. We present a novel approach to the extraction of features capable of supporting an explanation through the use of trajectory mining - extracting key features from the populations of NDAs. We apply Principal Components Analysis techniques to identify new methods of population diversity tracking post-runtime after projection into a lower dimensional space. These methods are applied to a set of benchmark problems solved by a Genetic Algorithm and a Univariate Estimation of Distribution Algorithm. We show that the new sub-space derived metrics can capture key learning steps in the algorithm run and how solution variable patterns that explain the fitness function may be captured in the principal component coefficients.

Citation

FYVIE, M., MCCALL, J.A.W. and CHRISTIE, L.A. 2021. Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms. In Bramer, M. and Ellis, R (eds.) Artificial intelligence XXXVIII: proceedings of 41st British Computer Society's Specialist Group on Artificial Intelligence (SGAI) Artificial intelligence international conference 2021 (AI-2021) (SGAI-AI 2021), 14-16 December 2021, [virtual conference]. Lecture notes in computer science, 13101. Cham: Springer [online], pages 89-102. Available from: https://doi.org/10.1007/978-3-030-91100-3_7

Conference Name 41st British Computer Society's Specialist Group on Artificial Intelligence (SGAI) Artificial intelligence international conference 2021 (AI-2021)
Conference Location [virtual conference]
Start Date Dec 14, 2021
End Date Dec 16, 2021
Acceptance Date Aug 31, 2021
Online Publication Date Dec 6, 2021
Publication Date Dec 31, 2021
Deposit Date Sep 16, 2021
Publicly Available Date Dec 6, 2021
Publisher Springer
Volume 13101
Pages 89-102
Series Title Lecture notes in artificial intelligence
Series Number 13101
Series ISSN 0302-9743
Book Title Artificial intelligence XXXVIII: proceedings of 41st British Computer Society's Specialist Group on Artificial Intelligence (SGAI) Artificial intelligence international conference 2021 (AI-2021) (SGAI-AI 2021), 14-16 December 2021, [virtual conference]
ISBN 9783030910990
DOI https://doi.org/10.1007/978-3-030-91100-3_7
Keywords Evolutionary algorithms; PCA; Explainability; Population diversity
Public URL https://rgu-repository.worktribe.com/output/1457054

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Copyright Statement
This pre-copyedited version is made available under the Springer terms of reuse for AAMs: https://www.springer.com/gp/open-access/publication-policies/aam-terms-of-use.







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