Associate Professor Marcus Gallagher
Associate Professor
School of Electrical Engineering and Computer Science
+61 7 336 56197
Researcher biography
Marcus Gallagher is an Associate Professor in the School of Electrical Engineering and Computer Science. His research interests are in the foundations of artificial intelligence, including optimisation and machine learning algorithms. He is particularly interested in understanding the relationship between algorithm performance and problem structure via benchmarking. His research includes cross-disciplinary collaborations and real-world applications of AI techniques in areas such as healthcare and computer networks.
Dr Gallagher received his BCompSc and GradDipSc from the University of New England, Australia in 1994 and 1995 respectively, and his PhD in 2000 from the University of Queensland, Australia. He also completed a GradCert (Higher Education) in 2010.
Publications
Book Chapter
Yuan, B. and Gallagher, M. (2007). Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks. Parameter Setting in Evolutionary Algorithms. (pp. 121-142) edited by Lobo, F. G., Lima, C. F. and Michalewicz, Z.. Berlin, Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-540-69432-8_6
Journal Articles
Manocchio, Liam Daly, Layeghy, Siamak, Gallagher, Marcus and Portmann, Marius (2025). An empirical evaluation of preprocessing methods for machine learning based network intrusion detection systems. Engineering Applications of Artificial Intelligence, 158 111289, 1-16. doi: 10.1016/j.engappai.2025.111289
Layeghy, 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
Sarhan, 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
Schrum, 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
Sarhan, 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
Suckling, 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
Caldwell, 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
Saleem, 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
Mayfield, 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
Symons, Martyn, Feeney, Gerald F. X., Gallagher, Marcus R., Young, Ross Mc D. and Connor, Jason P. (2020). Predicting alcohol dependence treatment outcomes: A prospective comparative study of clinical psychologists vs ‘trained’ machine learning models. Addiction, 115 (11) add.15038, 2164-2175. doi: 10.1111/add.15038
Hu, Xuelei, Gallagher, Marcus, Loveday, William, Dev, Abhilash and Connor, Jason P. (2019). Network analysis and visualisation of opioid prescribing data. IEEE Journal of Biomedical and Health Informatics, 24 (5) 8822723, 1-9. doi: 10.1109/jbhi.2019.2939028
Symons, Martyn, Feeney, Gerald F.X., Gallagher, Marcus R., Young, Ross McD. and Connor, Jason P. (2019). Machine learning vs addiction therapists: a pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication. Journal of Substance Abuse Treatment, 99, 156-162. doi: 10.1016/j.jsat.2019.01.020
Tamura, Kenichi and Gallagher, Marcus (2019). Quantitative measure of nonconvexity for black-box continuous functions. Information Sciences, 476, 64-82. doi: 10.1016/j.ins.2018.10.009
Saleem, Sobia, Gallagher, Marcus and Wood, Ian (2018). Direct feature evaluation in black-box optimization using problem transformations. Evolutionary Computation, 27 (1), 75-98. doi: 10.1162/evco_a_00247
Pedroso, Dorival M., Bonyadi, Mohammad Reza and Gallagher, Marcus (2017). Parallel evolutionary algorithm for single and multi-objective optimisation: Differential evolution and constraints handling. Applied Soft Computing, 61, 995-1012. doi: 10.1016/j.asoc.2017.09.006
Sardi, Junainah, Mithulananthan, N., Gallagher, M. and Hung, Duong Quoc (2017). Multiple community energy storage planning in distribution networks using a cost-benefit analysis. Applied Energy, 190, 453-463. doi: 10.1016/j.apenergy.2016.12.144
Mayfield, Helen, Smith, Carl, Gallagher, Marcus and Hockings, Marc (2017). Use of freely available datasets and machine learning methods in predicting deforestation. Environmental Modelling and Software, 87, 17-28. doi: 10.1016/j.envsoft.2016.10.006
Bosman, Peter A. N. and Gallagher, Marcus (2016). The importance of implementation details and parameter settings in black-box optimization: a case study on Gaussian estimation-of-distribution algorithms and circles-in-a-square packing problems. Soft Computing, 22 (4), 1-15. doi: 10.1007/s00500-016-2408-3
Gallagher, Marcus (2016). Towards improved benchmarking of black-box optimization algorithms using clustering problems. Soft Computing, 20 (10), 1-15. doi: 10.1007/s00500-016-2094-1
Morgan, Rachel and Gallagher, Marcus (2015). Analysing and characterising optimization problems using length scale. Soft Computing, 21 (7), 1735-1752. doi: 10.1007/s00500-015-1878-z
Luo, Wei, Gallagher, Marcus, Loveday Bill, Ballantyne, Susan, Connor, Jason P. and Wiles, Janet (2014). Detecting contaminated birthdates using generalized additive models. BMC Bioinformatics, 15 (1) 185. doi: 10.1186/1471-2105-15-185
Morgan, Rachael and Gallagher, Marcus (2014). Sampling techniques and distance metrics in high dimensional continuous landscape analysis: limitations and improvements. IEEE Transactions On Evolutionary Computation, 18 (3) 6595542, 456-461. doi: 10.1109/TEVC.2013.2281521
Luo, Wei, Cao, Jiguo, Gallagher, Marcus R. and Wiles, Janet H. (2013). Estimating the intensity of ward admission and its effect on emergency department access block. Statistics In Medicine, 32 (15), 2681-2694. doi: 10.1002/sim.5684
Luo, Wei, Gallagher, Marcus and Wiles, Janet (2013). Parameter-free search of time-series discord. Journal of Computer Science and Technology, 28 (2), 300-310. doi: 10.1007/s11390-013-1330-8
Arief, Ardiaty, Dong, ZhaoYang, Nappu, Muhammad Bachtiar and Gallagher, Marcus (2013). Under voltage load shedding in power systems with wind turbine-driven doubly fed induction generators. Electric Power Systems Research, 96, 91-100. doi: 10.1016/j.epsr.2012.10.013
Morgan, R. and Gallagher, M. (2012). Using landscape topology to compare continuous metaheuristics: a framework and case study on EDAs and ridge structure. Evolutionary Computation, 20 (2), 277-299. doi: 10.1162/EVCO_a_00070
Chen, Ling, Liu, Yang, Gallagher, Marcus, Pailthorpe, Bernard, Sadiq, Shazia, Shen, Heng Tao and Li, Xue (2012). Introducing cloud computing topics in curricula. Journal of Information Systems Education, 23 (3), 315-324.
McPartland, Michelle and Gallagher, Marcus (2011). Reinforcement learning in first person shooter games. Ieee Transactions On Computational Intelligence and Ai in Games, 3 (1) 5672586, 43-56. doi: 10.1109/TCIAIG.2010.2100395
You, Liwen, Brusic, Vladimir, Gallagher, Marcus and Boden, Mikael (2010). Using Gaussian process with test rejection to detect T-Cell epitopes in pathogen genomes. IEEE-ACM Transactions on Computational Biology and Bioinformatics, 7 (4) 4695825, 741-751. doi: 10.1109/TCBB.2008.131
Yuan, Bo and Gallagher, Marcus (2007). Combining meta-EAs and racing for difficult EA parameter tuning tasks. Studies in Computational Intelligence, 54, 121-142. doi: 10.1007/978-3-540-69432-8_6
Gallagher, MR and Doherty, J (2007). Parameter interdependence and uncertainty induced by lumping in a hydrologic model. Water Resources Research, 43 (5) W05421. doi: 10.1029/2006WR005347
Yin, Hujun, Gallagher, Marcus and Magdon-Ismail, Malik (2006). Introduction. International Journal of Neural Systems, 16 (5), v-vi.
Rohde, D. J., Gallagher, M. R., Drinkwater, M. J. and Pimbblet, K. A. (2006). Matching of catalogues by probabilistic pattern classification. Monthly Notices of The Royal Astronomical Society, 369 (1), 2-14. doi: 10.1111/j.1365-2966.2006.10304.x
Gallagher, Marcus and Yuan, Bo (2006). A general-purpose tunable landscape generator. IEEE Transactions On Evolutionary Computation, 10 (5), 590-603. doi: 10.1109/TEVC.2005.863628
Gallagher, Marcus, Hogan, James and Maire, Frederic (2005). Lecture Notes in Computer Science: Preface. Lecture Notes in Computer Science, 3578
Rohde, D. J., Drinkwater, M. J., Gallagher, M. R., Downs, T. and Doyle, M. T. (2005). Applying machine learning to catalogue matching in astrophysics. Monthly Notices of The Royal Astronomical Society, 360 (1), 69-75. doi: 10.1111/j.1365-2966.2005.08930.x
Gallagher, M. and Frean, M. (2005). Population-based continuous optimization, probabilistic modelling and mean shift. Evolutionary Computation, 13 (1), 29-42. doi: 10.1162/1063656053583478
Yuan, Bo and Gallagher, Marcus (2004). Statistical racing techniques for improved empirical evaluation of evolutionary algorithms. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3242, 172-181.
Rohde, David, Drinkwater, Michael, Gallagher, Marcus, Downs, Tom and Doyle, Marianne (2004). Machine learning for matching astronomy catalogues. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3177, 702-707.
Gallagher, M. R. and Downs, T. (2003). Visualization of learning in multilayer perceptron networks using principal component analysis. IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Part B-cybernetics, 33 (1), 28-34. doi: 10.1109/TSMCB.2003.808183
Gallagher, Marcus, Downs, Tom and Wood, Ian (2002). Empirical evidence for ultrametric structure in multi-layer perceptron error surfaces. Neural Processing Letters, 16 (2), 177-186. doi: 10.1023/A:1019956303894
Gallagher, Marcus (2000). Empirical investigation of the user-parameters and performance of continuous PBIL algorithms. Neural Networks for Signal Processing - Proceedings of the IEEE Workshop, 2, 702-710.
Gallagher, Mark and Webb, William (1999). UMTS: The next generation of mobile radio. IEE Review, 45 (2), 59-63.
Bartlett, A., Darnell, M. and Gallagher, M. (1996). A flexible, low-cost, ionospheric sounding system. IEE Colloquium (Digest) (24)
Ripley, M. W., Darnell, M. and Gallagher, M. (1996). An embedded HF frequency management system. IEE Colloquium (Digest) (24)
Piggin, P. W., Darnell, M. and Gallagher, M. (1996). Passive monitoring for improved HF frequency management. IEE Colloquium (Digest) (24)
Gallagher, Mark (1996). The XK8 engine management system and electronic engine control module. IEE Colloquium (Digest) (281)
Pulko, S. H., Wilkinson, A. J. and Gallagher, M. (1993). Redundancy and its implications in TLM diffusion models. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 6 (2), 135-144. doi: 10.1002/jnm.1660060206
Conference Papers
Vermetten, Diederick, Dinu, Catalin-Viorel and Gallagher, Marcus (2025). A Standardized Benchmark Set of Clustering Problem Instances for Comparing Black-Box Optimizers. New York, NY, USA: ACM. doi: 10.1145/3729878.3746699
Hajari, Sara and Gallagher, Marcus (2024). Searching for Benchmark Problem Instances from Data-Driven Optimisation. New York, NY, USA: ACM. doi: 10.1145/3638530.3654322
Gallagher, Marcus and Munoz, Mario (2024). Towards an improved understanding of features for more interpretable landscape analysis. GECCO '24: Genetic and Evolutionary Computation Conference Companion, Melbourne, VIC Australia, 14 - 18 July 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3638530.3654301
Qiao, Yukai and Gallagher, Marcus (2024). Analyzing the Runtime of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) on the Concatenated Trap Function. GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion, Melbourne, VIC Australia, 14 - 18 July 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3638530.3664158
Qiao, 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
Munn, 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
Dewanto, 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
Bishop, 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
Lo, 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
Lo, 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
Bishop, 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
Tsuchida, 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. doi: 10.1609/aaai.v35i11.17197
Tsuchida, 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.
Bishop, 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
Qiao, 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
du 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
Van 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
Roberts, 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.
Rapin, 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
Gallagher, 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
Tsuchida, 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
Saleem, Sobia, Gallagher, Marcus and Wood, Ian (2018). A model-based framework for black-box problem comparison using gaussian processes. 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018, Coimbra, Portugal, 8-12 September 2018. Cham, Switzerland: Springer Verlag. doi: 10.1007/978-3-319-99259-4_23
Tsuchida, Russell, Roosta-Khorasani, Farbod and Gallagher, Marcus (2018). Invariance of weight distributions in rectified MLPs. 35th International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018. Cambridge, MA, United States: M I T Press.
du Preez-Wilkinson, Nathaniel, Gallagher, Marcus and Hu, Xuelei (2018). Flood-fill Q-learning updates for learning redundant policies in order to interact with a computer screen by clicking. 31st Australasian Joint Conference on Artificial Intelligence, AI 2018, Wellington,, December 11, 2018-December 14, 2018. Germany: Springer Verlag. doi: 10.1007/978-3-030-03991-2_49
du Preez-Wilkinson, Nathaniel, Gallagher, Marcus and Hu, Xuelei (2018). Intra-task curriculum learning for faster reinforcement learning in video games. 31st Australasian Joint Conference on Artificial Intelligence (AI 2018), Wellington, New Zealand, 11-14 December 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_6
Saleem, Sobia and Gallagher, Marcus (2017). Exploratory analysis of clustering problems using a comparison of particle swarm optimization and differential evolution. 3rd Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017, Geelong, VIC, Australia, 31 January – 2 February 2017. Heidelberg, Germany: Springer . doi: 10.1007/978-3-319-51691-2_27
Hu, Xuelei, Gallagher, Marcus, Loveday, William, Connor, Jason P. and Wiles, Janet (2015). Detecting anomalies in controlled drug prescription data using probabilistic models. 1st Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2015, Newcastle, NSW Australia, 5 - 7 February 2015. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-14803-8_26
Mishra, Krishna Manjari and Gallagher, Marcus (2014). A modified screening estimation of distribution algorithm for large-scale continuous optimization. 10th International Conference SEAL 2014, Dunedin, New Zealand, 15-18 December 2014. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-13563-2_11
Gallagher, Marcus (2014). Clustering problems for more useful benchmarking of optimization algorithms. 10th International Conference SEAL 2014, Dunedin, New Zealand, 15-18 December 2014. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-13563-2_12
Morgan, Rachael and Gallagher, Marcus (2014). Fitness landscape analysis of circles in a square packing problems. 10th International Conference, SEAL 2014, Dunedin, New Zealand, 15 - 18 December 2014. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-13563-2_39
Shaker, Noor, Togelius, Julian, Yannakakis, Georgios N., Poovanna, Likith, Ethiraj, Vinay S., Johansson, Stefan J., Reynolds, Robert G., Heether, Leonard K., Schumann, Tom and Gallagher, Marcus (2013). The Turing test track of the 2012 Mario AI championship: entries and evaluation. 2013 IEEE Conference on Computational Intelligence in Games (CIG), Niagara Falls, ON, Canada, 11-13 August, 2013. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/CIG.2013.6633634
Rohde, David, Gallagher, Marcus and Drinkwater, Michael (2012). Astronomical catalogue matching as a mixture model problem. 11th Brazilian Meeting on Bayesian Statistics (EBEB), Amparo, Brazil, 18-22 March 2012. College Park, MD, USA: American Institute of Physics. doi: 10.1063/1.4759615
McPartland, Michelle and Gallagher, Marcus (2012). Interactively training first person shooter bots. IEEE International Conference on Computational Intelligence and Games, CIG 2012, Granada, Spain, 11 - 14 September 2012. Piscataway, NJ, United States: IEEE (Institute of Electrical and Electronics Engineers). doi: 10.1109/CIG.2012.6374149
McPartland, Michelle and Gallagher, Marcus (2012). Game designers training first person shooter bots. AI 2012: Advances in Artificial Intelligence, Sydney, Australia, 4 - 7 December 2012. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-35101-3_34
Morgan, Rachael and Gallagher, Marcus (2012). Length scale for characterising continuous optimization problems. Parallel Problem Solving from Nature - PPSN XII 12th International Conference, Taormina, Italy, 1 - 5 September 2012. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-32937-1_41
Gallagher, Marcus (2012). Beware the parameters: estimation of distribution algorithms applied to circles in a square packing. Parallel Problem Solving from Nature - PPSN XII 12th International Conference, Taormina, Italy, 1 - 5 September 2012. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-32964-7_48
Mishra, Krishna Manjari and Gallagher, Marcus (2012). Variable screening for reduced dependency modelling in Gaussian-based continuous estimation of distribution algorithms. 2012 IEEE World Congress on Computational Intelligence (IEEE-WCCI 2012), Brisbane, QLD Australia, 10-15 June 2012. Piscataway, NJ United States: IEEE. doi: 10.1109/CEC.2012.6256482
Arief, Ardiaty, Nappu, Muhammad Bachtiar, Gallagher, Marcus and Dong, Zhao Yang (2011). Under voltage load shedding utilizing trajectory sensitivity to enhance voltage stability. 21st Australasian Universities Power Engineering Conference (AUPEC) 2011, Brisbane, Australia, 25-28 September 2011. Pitscataway, NJ, United States: IEEE.
Luo, Wei and Gallagher, Marcus (2011). Faster and parameter-free discord search in quasi-periodic time series. 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Shenzhen, China, 24-27 May 2011. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-20847-8_12
Arief, Ardiaty, Nappu, Muhammad Bachtiar, Gallagher, Marcus, Dong, Zhao Yang and Zhao, Junhua (2010). Comparison of CPF and modal analysis methods in determining effective DG locations. 9th International Power and Energy Conference (IPEC), Singapore, 27-29 October 2010. United States: IEEE. doi: 10.1109/IPECON.2010.5697057
Morgan, Rachael and Gallagher, Marcus (2010). When does dependency modelling help? Using a randomized landscape generator to compare algorithms in terms of problem structure. Parallel Problem Solving from Nature, Kraków, Poland, 11-15 September 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-15844-5_10
Luo, Wei and Gallagher, Marcus (2010). Unsupervised DRG upcoding detection in healthcare databases. IEEE International Conference on Data Mining, Sydney, NSW, Australia, 14-17 December 2010. Piscataway, NJ, U.S.A.: IEEE Computer Society. doi: 10.1109/ICDMW.2010.108
Luo, Wei, Gallagher, Marcus, O'Kane, Di, Connor, Jason, Dooris, Mark, Roberts, Col, Mortimer, Lachlan and Wiles, Janet (2010). Visualising a state-wide patient data collection: A case study to expand the audience for healthcare data. HIKM 2010: 4th Australasian Workshop on Health Informatics and Knowledge Management, Brisbane, Australia, 18-21 January 2010. Sydney, Australia: Australian Computer Society.
Yuan, Bo and Gallagher, Marcus (2009). Convergence analysis of UMDAc with finite populations: A case study on flat landscapes. 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009, Montréal, QC, Canada, 8-12 July 2009. New York, NY, U.S.A.: ACM (Association for Computing Machinery) Press. doi: 10.1145/1569901.1569967
Gallagher, Marcus (2009). Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noiseless function testbed. 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference (GECCO'09), Montreal, Canada, 8-12 July 2009. New York, United States: ACM Digital Library. doi: 10.1145/1570256.1570332
Yuan, Bo and Gallagher, Marcus (2009). An improved small-sample statistical test for comparing the success rates of evolutionary algorithms. 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009, Montreal, QC, Canada, 8-12 July 8 2009. New York, NY, United States: ACM. doi: 10.1145/1569901.1570213
Marcus Gallagher (2009). Investigating circles in a square packing problems as a realistic benchmark for continuous metaheuristic optimization algorithms. The VIII Metaheuristic International Conference MIC 2009, Hamburg, Germany, 13-16 July, 2009.
Gallagher, Marcus R. (2009). Black-Box Optimization Benchmarking: Results for the BayEDAcGAlgorithm on the Noiseless Function Testbed. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/1570256.1570318
McPartland, M. and Gallagher, M. (2008). Creating a multi-purpose first person shooter bot with reinforcement learning. IEEE Symposium on Computational Intelligence and Games 2008 (CIG '08), Perth, Australia, 15-18 December 2008. Piscataway, NJ, U.S.A.: IEEE. doi: 10.1109/CIG.2008.5035633
Wirth, N. and Gallagher, M. (2008). An influence map model for playing Ms. Pac-Man. IEEE Symposium on Computational Intelligence and Games 2008 (CIG '08), Perth, Australia, 15-18 December 2008. Piscataway, NJ, U.S.A.: IEEE - Institute of Electrical Electronics Engineers Inc.. doi: 10.1109/CIG.2008.5035644
McPartland, M. and Gallagher, M. (2008). Learning to be a Bot: Reinforcement learning in shooter games. 4th Artifical Intelligence for Interactive Digital Entertainment Conference, Stanford, California, 22-24 October, 2008. USA: The AAAI Press.
Kumar, N. and Gallagher, M. (2008). Gaussian mixture models in estimations of distribution algotithms: Implementation details and experimental analysis. 12th Asia-Pacific Symposium on Intelligent and Evolutionary Systems (IES'08), Melbourne, Australia, 7-8 December 2008. Clayton, VIC, Australia: Monash University, Clayton School of Information Technology.
Yeh, F.Y-H. and Gallagher, M. (2008). An empirical study of the sample size variability of optimal active learning using Gaussian process regression. IEEE World Congress on Computational Intelligence, Hong Kong, 1-6 June 2008. Piscataway NJ USA: IEEE. doi: 10.1109/IJCNN.2008.4634342
Gallagher, M. and Ledwich, M. (2007). Evolving pac-man players: Can we learn from raw input?. 2007 IEEE Symposium Series on Computational Intelligence and Games (IEEE SSCI 2007), Honolulu, Hawaii, 1-5 April, 2007. United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/CIG.2007.368110
Maetschke, S., Gallagher, M. and Boden, M. (2007). A comparison of sequence kernels for localization prediction of transmembrane proteins. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 2007 (CIBCB 2007), Honolulu, Hawaii, 1-5 April 2007. Piscataway, NJ, U.S.A.: IEEE - Institute of Electrical Electronics Engineers Inc.. doi: 10.1109/cibcb.2007.4221246
Connelly, S., Lindsay, P. A. and Gallagher, M. (2007). An agent based approach to examining shared situation awareness. 12th IEEE International Conference on Engineering Complex Computer Systems (ICECCS 2007), Auckland, New Zealand, 11-14 July 2007. Los Alamitos, CA, U.S.A.: IEEE Computer Society. doi: 10.1109/ICECCS.2007.14
Gallagher, M. R., Wood, I., Keith, J. and Sofronov, G. (2007). Bayesian inference in estimation of distribution algorithms. IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, 25-28 September 2007. Piscataway, NJ, U.S.A.: IEEE - Institute of Electrical Electronics Engineers Inc.. doi: 10.1109/CEC.2007.4424463
Yuan, Bo and Gallagher, Marcus (2007). Combining Meta-EAs and racing for difficult EA parameter tuning tasks. Workshop on Parameter Setting in Genetic and Evolutionary Algorithms, Washington Dc, 2005. BERLIN: SPRINGER-VERLAG BERLIN.
Yuan, Bo and Gallagher, Marcus (2006). A mathematical modelling technique for the analysis of the dynamics of a simple continuous EDA. 2006 IEEE Congress on Evolutionary Computation, CEC 2006, , , July 16, 2006-July 21, 2006.
Gallagher, M. R. and Yuan, B. (2006). A mathematical modelling technique for the analysis of the dynamics of a simple continuous EDA. 2006 IEEE Congress on Evolutionary Computation (CEC 2006), Vancouver, Canada, 16-21 July 2006. Piscataway, NJ, U.S.A.: IEEE - Institute of Electrical Electronics Engineers Inc.. doi: 10.1109/CEC.2006.1688497
Maetschke, S. R., Boden, M B and Gallagher, M R (2006). Higher order HMMs for localization prediction of transmembrance proteins. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006), Hobart, Australia, 4 December, 2006. New South Wales, Australia: Australian Computer Society Inc..
Yuan, Bo and Gallagher, Marcus (2005). A hybrid approach to parameter tuning in genetic algorithms. 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005, , , September 2, 2005-September 5, 2005.
Yuan, Bo and Gallagher, Marcus (2005). Experimental results for the special session on real-parameter optimization at CEC 2005: A simple, continuous EDA. 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005, , , September 2, 2005-September 5, 2005.
Yuan, B., Gallagher, M. R. and Crozier, S. (2005). MRI magnet design: Search space analysis, EDAs and a real-world problem with significant dependencies. 7th Annual Genetic and Evolutionary Computation Conference - GELCCO 2005, Washington DC, USA, 25-29 June, 2005. New York, USA: ACM Press. doi: 10.1145/1068009.1068362
Yuan, B. and Gallagher, M. R. (2005). On the importance of diversity maintenance in estimation of distribution algorithms. 7th Annual Genetic and Evolutionary Computation Conference GECCO 2005, Washington DC, USA, 25-29 June, 2005. New York, USA: ACM Press. doi: 10.1145/1068009.1068129
Yuan, B. and Gallagher, M. R. (2005). A hybrid approach to parameter tuning in genetic algorithms. 2005 IEEE Congress on Evolutionary Computation (IEEE CEC 2005), Edinburgh, Scotland, 2-5 September 2005. U.S.A.: IEEE.
Yeh, Y. and Gallagher, M. R. (2005). An empirical study of Hoelfding Racing for model selction in K-nearest neighbor classification. Intelligent Data Engineering and Automated Learning - IDEAL205, Brisbane, Australia, 6-8 July, 2005. Berlin, Germany: Springer. doi: 10.1007/11508069_29
Yuan, B. and Gallagher, M. R. (2005). Experimental results for the special session on real-parameter optimization at CEC 2005: A Simple, Continuous EDA. 2005 IEEE Congress on Evolutionary Computation (IEEE CEC 2005), Edinburgh, Scotland, 2-5 September, 2005. U.S.A.: IEEE.
Rohde, D. J., Drinkwater, M. J., Gallagher, M. R., Downs, T. and Doyle, M. T. (2004). Machine learning for matching astronomy catalogues. The Fifth International Intelligent Data Engineering and Automated Learning Conference (IDEAL 2004), Exeter, U.K., 25-27 August 2004. Berlin, Germany: Springer.
Yuan, B. and Gallagher, M. R. (2004). Statistical racing techniques for improved empirical evaluation of evolutionary algorithms. The Eighth International Conference on Parallel Problem Solving from Nature, Birmingham, U.K., 18-22 September 2004. Berlin: Springer-Verlag.
Leong, W. Y., Homer, J. P. and Gallagher, M. R. (2003). Blind separation of noisy mixtures using the SAND algorithm. The Seventh International Symposium on DSP for Communication System and the Second Workshop on the Internet, Telecommunication and Signal Processing, Coolangatta, 8-11 Decmber, 2003. Wollongong: The University of Wollongong.
Yuan, B. and Gallagher, M. R. (2003). Playing in continuous spaces: Some analysis and extension of population-based incremental learning. 2003 Congress on Evolutionary Computation (CEC '03), Canberra, Australia, 8-12 December 2003. Piscataway, NJ, U.S.A.: The Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2003.1299609
Yuan, B. and Gallagher, M. R. (2003). On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator. The 2003 Congress on Evolutionary Computation (CEC '03), Canberra, Australia, 8-12 December 2003. Piscataway, NJ, U.S.A.: The Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2003.1299610
Gallagher, M. R. and Ryan, A. J. (2003). Learning to play Pac-Man: An evolutionary, rule-based approach. The 2003 Congress on Evolutionary Computation (CEC 2003), Canberra, Australia, 8-12 December 2003. Piscataway, NJ, U.S.A.: The Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2003.1299397
Gallagher, M. R. and Deacon, P. (2002). Neural networks and the classification of mineralogical samples using x-ray spectra. Ninth International Conference on Neural Information Processing, Singapore, 18-22 November, 2002. Piscataway, NJ: The Institute of Electrical and Electronics Engineers. doi: 10.1109/ICONIP.2002.1201983
Gallagher, M. R. (2001). Fitness distance correlation of neural network error surfaces: A scalable, continuous optimization problem. Twelfth European Conference on Machine Learning, Freiburg, Germany, 3-7 September, 2001. Berlin: Springer-Verlag.
Gallagher, M. R. (2000). An empirical investigation of the user-parameters and performance of continuous PBIL algorithms. NNSP 2000, Sydney, NSW Australia, 11-13 December 2000. Piscataway, NJ United States: IEEE. doi: 10.1109/nnsp.2000.890149
Gallagher, M., Frean, M. and Downs, T. (1999). Real-valued evolutionary optimization using a flexible probability density estimator. GECCO-99, Orlando, Florida, 13-17 July, 1999. San Francisco: Morgan Kaufmann Publishers.
Ripley, M. W., Darnell, M. and Gallagher, M. (1996). Embedded HF frequency management system. IEE.
Bartlett, A., Darnell, M. and Gallagher, M. (1996). Flexible, low-cost, ionospheric sounding system. IEE.
Piggin, P. W. and Gallagher, M. (1995). Channel evaluation from predicted zero crossing analysis. IEEE.
Ripley, M., Gallagher, M. and Darnell, M. (1995). HF DSP based frequency management system. Proceeding of the 6th International Conference on Radio Receivers and Associated Systems, , , September 26, 1995-September 27, 1995. IEE.
Bartlett, A., Gallagher, M. and Darnell, M. (1995). Spectrally efficient oblique ionospheric sounding. IEE.
Ripley, M., Gallagher, M. and Darnell, M. (1994). Dual ionospheric sounding system monitor for HF RTCE. Publ by IEE.
Bartlett, A., Gallagher, M. and Darnell, M. (1994). Extraction, analysis and interpretation of digital ionograms. Publ by IEE.
Quirke, T. M., Yung, H. M., Darnell, M. and Gallagher, M. (1994). In-band multi-user transmission schemes for HF communications. Publ by IEE.
Gallagher, M. and Darnell, M. (1991). Economic ionospheric sounding system using standard HF radio system elements. Publ by IEE.
Gallagher, M. and Darnell, M. (1991). Propagation and interference measurements for use in real-time frequency management. Publ by IEE.
Riley, N. G., Gallagher, M. and Prasad, K. V. (1990). Simple model of intermodulation spectra for use on a personal computer. Publ by IEE.
Honary, B., Darnell, M., Zolghadr, F. and Gallagher, M. (1990). Synchronisation techniques for dispersive time-variable channels. Publ by IEE.
Thesis
Gallagher, Marcus Reginald (2000). Multi-layer perceptron error surfaces : visualization, structure and modelling. PhD Thesis, School of Computer Science and Electrical Engineering, The University of Queensland. doi: 10.14264/157842
Reference Entries
Gallagher, M. R. (2005). McCulloch-Pitts Network.
Gallagher, M. R. (2005). Perceptron.