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Towards explainable metaheuristics: feature extraction from trajectory mining.

Fyvie, Martin; McCall, John A.W.; Christie, Lee A.; Brownlee, Alexander E.I.; Singh, Manjinder


Alexander E.I. Brownlee

Manjinder Singh


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 article, 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.


FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A., BROWNLEE, A.E.I. and SINGH, M. [2023]. Towards explainable metaheuristics: feature extraction from trajectory mining. Expert systems [online], Early View. Available from:

Journal Article Type Article
Acceptance Date Sep 15, 2023
Online Publication Date Nov 2, 2023
Deposit Date Nov 3, 2023
Publicly Available Date Nov 3, 2023
Journal Expert systems
Print ISSN 0266-4720
Electronic ISSN 1468-0394
Publisher Wiley
Peer Reviewed Peer Reviewed
Keywords Metaheuristics; Evolutionary algorithms; Principle components analysis; Population diversity
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