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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Towards Explainable Metaheuristics: Feature Extraction from Trajectory Mining
Author(s): Fyvie, Martin
Mccall, John
Christie, Lee
Brownlee, Alexander
Singh, Manjinder
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Keywords: Evolutionary Algorithms
Population Diversity
Issue Date: 2-Nov-2023
Date Deposited: 1-Dec-2023
Citation: Fyvie M, Mccall J, Christie L, Brownlee A & Singh M (2023) Towards Explainable Metaheuristics: Feature Extraction from Trajectory Mining. <i>Expert Systems</i>, Art. No.: e13494.
Abstract: Explaining the decisions made by population-based metaheuristics can often be considered difficult due to the stochastic nature of the mechanisms employed by these optimisation methods. As industries continue to adopt these methods in areas that increasingly require end-user input and confirmation, the need to explain the internal decisions being made has grown. In this paper we present our approach to the extraction of explanation supporting features using trajectory mining. This is achieved through the application of Principal Components Analysis techniques to identify new methods of tracking population diversity changes post-runtime. The algorithm search trajectories were generated by solving a set of benchmark problems with a Genetic Algorithm and a Univariate Estimation of Distribution Algorithm and retaining all visited candidate solutions which were then projected to a lower dimensional sub-space. We also varied the selection pressure placed on high fitness solutions by altering the selection operators. Our results show that metrics derived from the projected sub-space algorithm search trajectories are capable of capturing key learning steps and how solution variable patterns that explain the fitness function may be captured in the principal component coefficients. A comparative study of variable importance rankings derived from a surrogate model built on the same dataset was also performed. The results show that both approaches are capable of identifying key features regarding variable interactions and their influence on fitness in a complimentary fashion.
DOI Link: 10.1111/exsy.13494
Rights: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2023 The Authors. Expert Systems published by John Wiley & Sons Ltd
Notes: Received: Added at production Revised: Added at production Accepted: Added at production DOI: xxx/xxxx ARTICLE TYPE
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