Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms.
Fyvie, Martin; McCall, John A.W.; Christie, Lee A.
Professor John McCall firstname.lastname@example.org
Dr Lee Christie email@example.com
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.
FYVIE, M., MCCALL, J.A.W. and CHRISTIE, L.A. . Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms. To be presented at 41st British Computer Society's Specialist Group on Artificial Intelligence (SGAI) Artificial intelligence international conference 2021 (AI-2021), 14-16 December 2021, [virtual conference].
|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|
|Deposit Date||Sep 16, 2021|
|Series Title||Lecture notes in artificial intelligence|
|Keywords||Evolutionary algorithms; PCA; Explainability; Population diversity|
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