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2024 Conference Publication Analyzing the Runtime of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) on the Concatenated Trap FunctionQiao, Yukai and Gallagher, Marcus (2024). Analyzing the Runtime of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) on the Concatenated Trap Function. New York, NY, USA: ACM. doi: 10.1145/3638530.3664158 |
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2024 Conference Publication Searching for Benchmark Problem Instances from Data-Driven OptimisationHajari, Sara and Gallagher, Marcus (2024). Searching for Benchmark Problem Instances from Data-Driven Optimisation. New York, NY, USA: ACM. doi: 10.1145/3638530.3654322 |
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2024 Journal Article Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasetsLayeghy, Siamak, Gallagher, Marcus and Portmann, Marius (2024). Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasets. Journal of Information Security and Applications, 80 103689, 1-18. doi: 10.1016/j.jisa.2023.103689 |
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2024 Journal Article Feature extraction for machine learning-based intrusion detection in IoT networksSarhan, Mohanad, Layeghy, Siamak, Moustafa, Nour, Gallagher, Marcus and Portmann, Marius (2024). Feature extraction for machine learning-based intrusion detection in IoT networks. Digital Communications and Networks, 10 (1), 205-216. doi: 10.1016/j.dcan.2022.08.012 |
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2023 Conference Publication Modularity based linkage model for neuroevolutionQiao, Yukai and Gallagher, Marcus (2023). Modularity based linkage model for neuroevolution. GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, 15-19 July 2023. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3583133.3590648 |
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2023 Conference Publication Towards understanding the link between modularity and performance in neural networks for reinforcement learningMunn, Humphrey and Gallagher, Marcus (2023). Towards understanding the link between modularity and performance in neural networks for reinforcement learning. International Joint Conference on Neural Networks (IJCNN), Broadbeach, QLD Australia, 18-23 June 2023. New York, NY United States: IEEE Computer Society. doi: 10.1109/ijcnn54540.2023.10191234 |
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2023 Journal Article Guest editorial: special issue on evolutionary computation for gamesSchrum, Jacob, Liu, Jialin, Browne, Cameron, Ekárt, Anikó and Gallagher, Marcus (2023). Guest editorial: special issue on evolutionary computation for games. IEEE Transactions on Games, 15 (1), 1-4. doi: 10.1109/tg.2022.3225730 |
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2023 Journal Article From zero-shot machine learning to zero-day attack detectionSarhan, Mohanad, Layeghy, Siamak, Gallagher, Marcus and Portmann, Marius (2023). From zero-shot machine learning to zero-day attack detection. International Journal of Information Security, 22 (4), 947-959. doi: 10.1007/s10207-023-00676-0 |
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2023 Journal Article Opioid dispensing 2008–18: a Queensland perspectiveSuckling, Benita, Pattullo, Champika, Donovan, Peter, Gallagher, Marcus, Patanwala, Asad and Penm, Jonathan (2023). Opioid dispensing 2008–18: a Queensland perspective. Australian Health Review, 47 (2), 217-225. doi: 10.1071/ah22247 |
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2022 Conference Publication Examining average and discounted reward optimality criteria in reinforcement learningDewanto, Vektor and Gallagher, Marcus (2022). Examining average and discounted reward optimality criteria in reinforcement learning. 35th Australasian Joint Conference on Artificial Intelligence (AI), Perth, Australia, 5-9 December 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-031-22695-3_56 |
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2022 Journal Article An agile new research framework for hybrid human-AI teaming: trust, transparency, and transferabilityCaldwell, Sabrina, Sweetser, Penny, O'donnell, Nicholas, Knight, Matthew J., Aitchison, Matthew, Gedeon, Tom, Johnson, Daniel, Brereton, Margot, Gallagher, Marcus and Conroy, David (2022). An agile new research framework for hybrid human-AI teaming: trust, transparency, and transferability. ACM Transactions on Interactive Intelligent Systems, 12 (3) 17, 1-36. doi: 10.1145/3514257 |
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2022 Conference Publication Pittsburgh learning classifier systems for explainable reinforcement learning: comparing with XCSBishop, Jordan T., Gallagher, Marcus and Browne, Will N. (2022). Pittsburgh learning classifier systems for explainable reinforcement learning: comparing with XCS. Genetic and Evolutionary Computation Conference (GECCO), Boston, MA, United States, 9-13 July 2022. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3512290.3528767 |
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2022 Conference Publication Graph neural network-based android malware classification with jumping knowledgeLo, Wai Weng, Layeghy, Siamak, Sarhan, Mohanad, Gallagher, Marcus and Portmann, Marius (2022). Graph neural network-based android malware classification with jumping knowledge. 2022 IEEE Conference on Dependable and Secure Computing (DSC), Edinburgh, United Kingdom, 22-24 June 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/dsc54232.2022.9888878 |
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2022 Conference Publication E-GraphSAGE: a graph neural network based intrusion detection system for IoTLo, Wai Weng, Layeghy, Siamak, Sarhan, Mohanad, Gallagher, Marcus and Portmann, Marius (2022). E-GraphSAGE: a graph neural network based intrusion detection system for IoT. NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 25-29 April 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/noms54207.2022.9789878 |
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2021 Journal Article Using regression models for characterizing and comparing black box optimization problemsSaleem, Sobia and Gallagher, Marcus (2021). Using regression models for characterizing and comparing black box optimization problems. Swarm and Evolutionary Computation, 68 100981, 1-10. doi: 10.1016/j.swevo.2021.100981 |
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2021 Conference Publication A genetic fuzzy system for interpretable and parsimonious reinforcement learning policiesBishop, Jordan T., Gallagher, Marcus and Browne, Will N. (2021). A genetic fuzzy system for interpretable and parsimonious reinforcement learning policies. GECCO '21: Genetic and Evolutionary Computation Conference, Lille, France, 10 - 14 July, 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3449726.3463198 |
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2021 Conference Publication Avoiding kernel fixed points: Computing with ELU and GELU infinite networksTsuchida, Russell, Pearce, Tim, van der Heide, Chris, Roosta, Fred and Gallagher, Marcus (2021). Avoiding kernel fixed points: Computing with ELU and GELU infinite networks. 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Online, 2 - 9 February 2021. Menlo Park, CA United States: Association for the Advancement of Artificial Intelligence. |
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2021 Conference Publication Avoiding kernel fixed points: computing with ELU and GELU infinite networksTsuchida, Russell, Pearce, Tim, van der Heide, Chris, Roosta, Fred and Gallagher, Marcus (2021). Avoiding kernel fixed points: computing with ELU and GELU infinite networks. 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence, Electr Network, 2-9 February 2021. Washington, DC, United States: Association for the Advancement of Artificial Intelligence. |
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2020 Conference Publication Optimality-based analysis of xcsf compaction in discrete reinforcement learningBishop, Jordan T. and Gallagher, Marcus (2020). Optimality-based analysis of xcsf compaction in discrete reinforcement learning. 16th International Conference on Parallel Problem Solving from Nature PPSN 2020, Leiden, Netherlands, September 5-9, 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-58115-2_33 |
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2020 Journal Article Considerations for selecting a machine learning technique for predicting deforestationMayfield, Helen J. , Smith, Carl , Gallagher, Marcus and Hockings, Marc (2020). Considerations for selecting a machine learning technique for predicting deforestation. Environmental Modelling and Software, 131 104741, 1-10. doi: 10.1016/j.envsoft.2020.104741 |
