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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 Towards an Improved Understanding of Features for More Interpretable Landscape AnalysisGallagher, Marcus and Munoz, Mario (2024). Towards an Improved Understanding of Features for More Interpretable Landscape Analysis. Association for Computing Machinery, Inc. doi: 10.1145/3638530.3654301 |
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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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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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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 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 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 Conference Publication A novel mutation operator for variable length algorithmsVan Ryt, Saskia, Gallagher, Marcus and Wood, Ian (2020). A novel mutation operator for variable length algorithms. AI 2020: Advances in Artificial Intelligence: 33rd Australasian Joint Conference, Canberra, ACT, Australia, 29 - 30 November 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-64984-5_14 |
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2020 Conference Publication An Implementation and Experimental Evaluation of a Modularity Explicit Encoding Method for Neuroevolution on Complex Learning TasksQiao, Yukai and Gallagher, Marcus (2020). An Implementation and Experimental Evaluation of a Modularity Explicit Encoding Method for Neuroevolution on Complex Learning Tasks. 33rd Australasian Joint Conference, AI 2020, Canberra, ACT Australia, 29–30 November 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-64984-5_11 |
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2020 Conference Publication Fitness landscape features and reward shaping in reinforcement learning policy spacesdu Preez-Wilkinson, Nathaniel and Gallagher, Marcus (2020). Fitness landscape features and reward shaping in reinforcement learning policy spaces. Parallel Problem Solving from Nature – PPSN XVI, Leiden, The Netherlands, 5 - 9 September 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-58115-2_35 |
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2019 Conference Publication Reversible jump probabilistic programmingRoberts, David A., Gallagher, Marcus and Taimre, Thomas (2019). Reversible jump probabilistic programming. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), Naha, Okinawa, Japan, 16 - 18 April 2019. Brookline, MA, United States: ML Research Press. |
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2019 Conference Publication Exchangeability and kernel invariance in trained MLPsTsuchida, Russell, Roosta, Fred and Gallagher, Marcus (2019). Exchangeability and kernel invariance in trained MLPs. Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19, Macao, China, 10-16 August 2019. Marina del Rey, CA USA: International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2019/498 |
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2019 Conference Publication Exploring the MLDA benchmark on the Nevergrad platformRapin, Jeremy, Gallagher, Marcus, Kerschke, Pascal, Preuss, Mike and Teytaud, Olivier (2019). Exploring the MLDA benchmark on the Nevergrad platform. 2019 Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, 13 - 17 July 2019. New York, New York, USA: Association for Computing Machinery, Inc. doi: 10.1145/3319619.3326830 |
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2019 Conference Publication Fitness landscape analysis in data-driven optimization: An investigation of clustering problemsGallagher, Marcus (2019). Fitness landscape analysis in data-driven optimization: An investigation of clustering problems. IEEE Congress on Evolutionary Computation (IEEE CEC), Wellington, New Zealand, 10-13 June, 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2019.8790323 |
