Towards Explainable Metaheuristics: PCA for Trajectory Mining in Evolutionary Algorithms. - Martin Fyvie; John A. W. McCall; Lee A. Christie (SGAI Conf., 2021)

Non-Deterministic Solvers and Explainable AI through Trajectory Mining. - Martin Fyvie; John A. W. McCall; Lee A. Christie (SICSA XAI, 2021)

Explaining a Staff Rostering Genetic Algorithm using Sensitivity Analysis and Trajectory Analysis. - Martin Fyvie; John McCall 001; Lee A. Christie; Alexander E. I. Brownlee (GECCO Companion, 2023)

Explaining a Staff Rostering Problem by Mining Trajectory Variance Structures. - Martin Fyvie; John A. W. McCall; Lee A. Christie; Alexandru-Ciprian Zavoianu; Alexander E. I. Brownlee; Russell Ainslie (SGAI Conf., 2023)

Explaining a Staff Rostering Problem Using Partial Solutions. - Giancarlo Antonino Pasquale Ignazio Catalano; Alexander E. I. Brownlee; David E. Cairns; John A. W. McCall; Martin Fyvie; Russell Ainslie (SGAI Conf. (2), 2024)

Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems. - Ryan Zhou; Jaume Bacardit; Alexander E. I. Brownlee; Stefano Cagnoni; Martin Fyvie; Giovanni Iacca; John A. W. McCall; Niki van Stein; David Walker 003; Ting Hu 001 (CoRR, 2024)

Towards explainable metaheuristics: Feature extraction from trajectory mining. - Martin Fyvie; John A. W. McCall; Lee A. Christie; Alexander E. I. Brownlee; Manjinder Singh (Expert Syst. J. Knowl. Eng., 2025)

Towards Explainable Metaheuristics: Feature Mining of Search Trajectories through Principal Component Projection. - Martin Fyvie; John A. W. McCall; Lee A. Christie (ACM Trans. Evol. Learn. Optim., 2025)