Items where Subject is "Machine learning"

Book chapter

Nitschke, G (2020) Automating Automation: Lessons from R.U.R about the Future of Evolutionary Robotics, Robot 100, VŠCHT Praha.

Conference paper

Abdullahi, T and Nitschke, G (2021) Disease Outbreaks: Tuning Predictive Machine Learning, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2021).

Abdullahi, T and Nitschke, G (2021) Predicting Disease Outbreaks with Climate Data, Proceedings of IEEE Congress on Evolutionary Computation (IEEE CEC 2021).

Abramowitz, Sasha and Nitschke, Geoff (2022) Scalable Evolutionary Hierarchical Reinforcement Learning, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2022), 9-13 July 2022, Boston, USA.

Acton, S and Abramowitz, S and Toledo, L and Nitschke, E (2020) Efficiently Coevolving Deep Neural Networks and Data Augmentations, Proceedings of IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2020).

Furman, G and Nitschke, G (2021) Environmental Impact on Evolving Language Diversity, Proceedings of Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021).

Furman, G and Nitschke, G (2020) Evolving an Artificial Creole, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2020).

Furman, G and Nitschke, G (2021) The Role of Speaker Prestige in Synthetic Language Evolution, Proceedings of Conference on Artificial Life (ALIFE 2021).

Gower-Winter, Brandon and Nitschke, Geoff (2022) Do Harsher Environments cause Selfish or Altruistic Behavior, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2022), 9-13 July 2022, Boston, USA.

Gower-Winter, Brandon and Nitschke, Geoff (2022) Extreme Environments Perpetuate Cooperation, Proceedings of IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2022), 4-7 December 2022.

Hallauer, S and Nitschke, G (2020) Energy and Complexity in Evolving Collective Robot Bodies and Brains, Proceedings of IEEE Congress on Evolutionary Computation (IEEE CEC 2020).

Hallauer, S and Nitschke, G (2020) The Expense of Neuro-Morpho Functional Machines, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2020).

Huang, A and Nitschke, G (2020) Automating Coordinated Autonomous Vehicle Control, Proceedings of International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020).

Huang, A and Nitschke, G (2020) Evolutionary Automation of Coordinated Autonomous Vehicles, Proceedings of IEEE Congress on Evolutionary Computation (IEEE CEC 2020).

Maccallum, Rob and Nitschke, Geoff (2022) Automated Ligand Design in Simulated Molecular Docking, Proceedings of 2022 Conference on Artificial Life (ALIFE 2022), 18-22 July 2022, Online.

Mailer, C and Nitschke, G (2021) Evolving Gaits for Damage Control in a Hexapod Robot, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2021).

Spanellis, C and Stewart, B and Nitschke, G (2021) The Environment and Body-Brain Complexity, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2021).

Toussaint, W and Moodley, D (2020) Identifying optimal clustering structures for residential energy consumption patterns using competency questions, Proceedings of SAICSIT '20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020, 14-16 September 2020, ACM.

White, Michael and Marais, Patrick (2019) Supervised learning and image processing for efficient malaria detection, Proceedings of South African Forum for AI Research (FAIR), 4-6 December 2019, Cape Town, 161-172.

Journal article (online only)

Truda, Gianluca and Marais, Patrick (2021) Evaluating warfarin dosing models on multiple datasets with a novel software framework and evolutionary optimisation, Journal of Biomedical Informatics, 113, Elsevier.

Journal article (paginated)

Abdullahi, Tassallah and Nitschke, Geoff and Sweijd, Neville (2022) Predicting diarrhoea outbreaks with climate change, PLOS One, e0262008, 17, PLOS.

Nitschke, G and Howard, D (2021) AutoFac: The Perpetual Robot Machine, IEEE Transactions on Artificial Intelligence, IEEE.

Nudelman, Z and Moodley, D and Berman, S (2019) Using Bayesian Networks and Machine Learning to Predict Computer Science Success, 47th Annual Conference of the Southern African Computer Lecturers' Association, SACLA 2018 Gordon's Bay, South Africa, June 18–20, 2018 Revised Selected Papers, ICT Education, Communications in Computer and Information Science, 963, 207-222, Springer.