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- W3201010908 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." @default.
- W3201010908 created "2021-09-27" @default.
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- W3201010908 date "2021-01-01" @default.
- W3201010908 modified "2023-10-14" @default.
- W3201010908 title "Towards Explainable Metaheuristics: PCA for Trajectory Mining in Evolutionary Algorithms" @default.
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- W3201010908 doi "https://doi.org/10.1007/978-3-030-91100-3_7" @default.
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