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D. Wood, T. Mu, A Unified Theory of Diversity in Ensemble Learning, JMLR 2023

C. Shand, R. Allmendinger et al., HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis, preprint, arXiv:2102.06940

A. M. Webb, C. Reynolds et al, To Ensemble or Not Ensemble: When does End-To-End Training Fail?, ECML PKDD 2020, arXiv:1902.04422

C. Shand, R. Allmendinger et al., Evolving Controllably Difficult Datasets for Clustering, GECCO 2019

A. M. Webb, G. Brown, and M. Lujan, ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM, TAROS 2019

S. Saeedi, B. Bodin et al, Navigating the Landscape for Real-Time Localization and Mapping for Robotics and Virtual and Augmented Reality, Proceedings of the IEEE Vol. 106

A. M. Webb, On Selection for Evolvability. PhD thesis. School of Computer Science, University of Manchester, UK. Supervisors: Prof. Joshua Knowles and Dr Julia Handl

A. M. Webb, J. Handl, and J. Knowles, How Much Should You Select for Evolvability?, ECAL 2015

A. M. Webb and J. Knowles, Studying the Evolvability of Self-Encoding Genotype-Phenotype Maps, ALIFE 2014

A. M. Webb, S. Davies, and D. Lester, Spiking Neural PID Controllers, ICONIP 2011