Marcus Gallagher: Publications

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Book Chapters

Bo Yuan and Marcus Gallagher. Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks. In F. Lobo et al. (editors), Parameter Setting in Evolutionary Algorithms, Studies in Computational Intelligence Series, Springer, pp.121-142, 2007.

Journal Papers

M. McPartland and M. Gallagher. Reinforcement Learning in First Person Shooter Games. IEEE Transactions on Computational Intelligence and AI in Games 3(1):43-56, 2011.
L. You, V. Brusic, M. Gallagher, and M. Boden. Using Gaussian Process with Test Rejection to Detect T-Cell Epitopes in Pathogen Genomes. IEEE/ACM Transactions on Computational Biology and Bioinformatics 7(4):741-751, 2010.
M. Gallagher and B. Yuan. `A General-Purpose Tunable Landscape Generator. IEEE Transactions on Evolutionary Computation 10(5):590-603, 2006.
D. J. Rohde, M. R. Gallagher, M. J. Drinkwater and K. A. Pimbblet. Matching of Catalogues by Probabilistic Pattern Classification. Monthly Notices of the Royal Astronomical Society 369(1): 2-14, 2006. arXiv e-print
D. Rohde, M. Drinkwater, M. Gallagher, T. Downs and M. Doyle. Applying Machine Learning to Catalogue Matching in Astrophysics. Monthly Notices of the Royal Astronomical Society 360(1): 69-75, June 2005. arXiv e-print
M. and M. Frean. Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift. Evolutionary Computation 13(1): 29-42, 2005.
Marcus Gallagher and Tom Downs. Visualization of Learning in Multi-layer Perceptron Networks using PCA. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 33(1):28-34, 2003.
Marcus Gallagher, Tom Downs and Ian Wood. Empirical Evidence for Ultrametric Structure in Multi-layer Perceptron Error Surfaces. Neural Processing Letters, 16(2):177-186, 2002.

Conference Papers

W. Luo and M. Gallagher. Faster and Parameter-Free Discord Search in Quasi-Periodic Time Series. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2011), Springer, LNAI 6635, pp.135-148, 2011.
R. Morgan and M. Gallagher. When Does Dependency Modelling Help? Using a Randomized Landscape Generator to Compare Algorithms in Terms of Problem Structure. In Parallel Problem Solving from Nature--PPSN XI. Springer. pp. 94-103, 2010.
W. Luo and M. Gallagher. Unsupervised DRG Upcoding Detection in Healthcare Databases. In Proc. International Workshop on Data Mining Application in Government and Industry 2010 (International Conference on Data Mining Workshops 2010), pp.600-605, 2010.
W. Luo, M. Gallagher, D. O'Kane, J. Connor, M. Dooris, C. Roberts, L. Mortimer and J. Wiles. Visualising a State-wide Patient Data Collection: A Case Study to Expand the Audience for Healthcare Data. In Proc. Fourth Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2010). Conferences in Research and Practice in Information Technology, Volume 108, pp. 45-52, 2010.
M. Gallagher. Investigating Circles in a Square Packing Problems as a Realistic Benchmark for Continuous Metaheuristic Optimization Algorithms. In Proc. MIC 2009: The VIII Metaheuristics International Conference, 2009.
M. Gallagher. Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noiseless function testbed. In Proc. 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers (GECCO '09). ACM, pp. 2281-2286. 2009.
M. Gallagher. Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noisy function testbed. In Proc. 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers (GECCO '09). ACM, pp. 2383-2388. 2009.
B. Yuan and M. Gallagher. Convergence analysis of UMDAC with finite populations: a case study on flat landscapes. In Proc. 11th Annual Conference on Genetic and Evolutionary Computation (GECCO '09). ACM, pp. 477-482. 2009.
N. Wirth and M. Gallagher. An Influence Map Model for Playing Ms. Pac-Man. In IEEE Symposium on Computational Intelligence and Games (CIG'08), pp.228-233, 2008. (This paper was awarded the Overall Best Paper Award at the Symposium)
M. McPartland and M. Gallagher. Creating a Multi-Purpose First Person Shooter Bot with Reinforcement Learning. In IEEE Symposium on Computational Intelligence and Games (CIG'08), pp.143-150, 2008.
N. Kumar and M. Gallagher. Gaussian Mixture Models in Estimations of Distribution Algorithms: Implementation Details and Experimental Analysis. In Proc. 12th Asia-Pacific Symposium on Intelligent and Evolutionary Systems (IES'08), pp.51-61, 2008.
M. McPartland and M. Gallagher. Learning to be a Bot: Reinforcement Learning in First Person Shooter Games. In Proc. Fourth Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-08) , 2008.
F. Y-H. Yeh and M. Gallagher. An Empirical Study of the Sample Size Variability of Optimal Active Learning Using Gaussian Process Regression. In Proc. IEEE International Joint Conference on Neural Networks (IJCNN), 2008.
M. Gallagher, I. Wood, J. Keith and G. Sofronov. Bayesian Inference in Estimation of Distribution Algorithms (updated version - September 2008). Prev. version appears in: Proc. IEEE Congress on Evolutionary Computation (CEC), pp.127-133, 2007.
[Note: The version of this paper published in the CEC'07 proceedings contains some minor typographic errors (specifically in Eqns (5) and (6) and in line 9 of Table III). Please refer to this updated version of the paper for the correct formulae (as actually used in the experiments presented in the paper). Other minor notational modifications have also been made.]
S. Connelly, P. Lindsay and M. Gallagher. An Agent Based Approach to Examining Shared Situation Awareness. In Proc. IEEE International Conference on Engineering of Complex Computer Systems (to appear), 2007.
Marcus Gallagher and Mark Ledwich. Evolving Pac-Man Players: Can We Learn from Raw Input? In Proc. IEEE Symposium on Computational Intelligence and Games, pp.282-287, 2007.
Stefan Maetschke, Marcus Gallagher and Mikael Boden. A Comparison of Sequence Kernels for Localization Prediction of Transmembrane Proteins. In Proc. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp.367-372, 2007.
S. Maetschke, M. Boden and M. Gallagher. Higher order HMMs for Localization Prediction of Transmembrane Proteins, In Proc. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006), Hobart, Australia. CRPIT, 73. Boden, M. and Bailey, T.L., Eds., ACS. 49-53.
B. Yuan and M. Gallagher. A Mathematical Modelling Technique for the Analysis of the Dynamics of a Simple Continuous EDA. Proceedings of 2006 Congress on Evolutionary Computation, pp. 1585-1591, 2006.
B. Yuan and M. Gallagher. Experimental Results for the Special Session on Real-Parameter Optimization at CEC 2005: A Simple, Continuous EDA. In Proceedings of the 2005 Congress on Evolutionary Computation (CEC'05), pp. 1792-1799, 2005.
B. Yuan and M. Gallagher. A Hybrid Approach to Parameter Tuning in Genetic Algorithms. To appear in Proceedings of the 2005 Congress on Evolutionary Computation (CEC'05), pp. 1096-1103, 2005.
B. Yuan and M. Gallagher. On the Importance of Diversity Maintenance in Estimation of Distribution Algorithms. In H-G. Beyer et al., editors, Proc. Genetic and Evolutionary Computation Conference (GECCO 2005), pp.719-726, 2005.
B. Yuan, M. Gallagher and S. Crozier. MRI Magnet Design: Search Space Analysis, EDAs and a Real-World Problem with Significant Dependences. In H-G. Beyer et al., editors, Proc. Genetic and Evolutionary Computation Conference (GECCO 2005), pp.2141-2148, 2005.
F. Y-H. Yeh and M. Gallagher. An Empirical Study of Heoffding Racing for Model Selection in k-Nearest Neighbor Classification. In M. Gallagher et al., editors, Proc. Sixth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2005), pp. 220-227.
B. Yuan and M. Gallagher. Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms. In X. Yao et al., editors, Proc. 8th International Conference on Parallel Problem Solving from Nature (PPSN'04), pp 172-181. Lecture Notes in Computer Science vol.3242, 2004.
D. Rohde, M. Drinkwater, M. Gallagher, T. Downs and M. Doyle. Machine Learning for Matching Astronomy Catalogues. In Z. R. Yang et al., editors, Proc. Fifth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'04). Lecture Notes in Computer Science vol.3177, pp 702-707. 2004.
B. Yuan and M. Gallagher. On Building a Principled Framework for Evaluating and Testing Evolutionary Algorithms: A Continuous Landscape Generator. In R. Sarkar et. al., editors, Proc. Congress on Evolutionary Computation (CEC), pp 451-458, 2003.
B. Yuan and M. Gallagher. Playing in Continuous Spaces: Some Analysis and Extension of Population-based Incremental Learning. In R. Sarkar et. al., editors, Proc. Congress on Evolutionary Computation (CEC), pp 443-450, 2003.
M. Gallagher and A. Ryan. Learning to Play Pac-Man: An Evolutionary, Rule-based Approach. In R. Sarkar et. al., editors, Proc. Congress on Evolutionary Computation (CEC), pp 2462-2469, 2003.
W. Y. Leong, J. Homer and M. Gallagher. Blind Separation of Noisy Mixtures Using the SAND Algorithm. In 7th International Symposium on DSP and Communication Systems (DSPCS), 2003.
M. Gallagher and P. Deacon. Neural Networks and the Classification of Mineralogical Samples Using X-Ray Spectra. In L. Wang et. al., editors, Proc. International Conference on Neural Information Processing (ICONIP'02), pp 2683-2687, 2002. IEEE Press, Piscataway, NJ.
M. Gallagher. Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem. In L. De Raedt and P. Flach (Eds.): European Conference on Machine Learning (ECML 2001), LNAI2167, pp. 157-166, 2001.
M. Gallagher. An Empirical Investigation of the User-Parameters and Performance of Continuous PBIL Algorithms. In B. Widrow et al., editors, Neural Networks for Signal Processing X (Proceedings of the 2000 IEEE Workshop), pp 702-710, 2000. IEEE Press, New York.
M. Gallagher, M. Frean and T. Downs. Real-Valued Evolutionary Optimization using a Flexible Probability Density Estimator. In W. Banzhaf et al., editors, Proc. Genetic and Evolutionary Computation Conference (GECCO'99), pp 840-846, 1999. Morgan Kaufmann Publishers, San Francisco, CA.
M. Gallagher, T. Downs and I. Wood. Ultrametric Structure in Autoencoder Error Surfaces. In L. Niklasson et al., editors, Proc. Eighth International Conference on Artificial Neural Networks (ICANN'98), pp 177-182, 1998. Springer, London.
M. Gallagher and T. Downs. On Ultrametricity in Feedforward Neural Network Error Surfaces. In T. Downs et al., editors, Proc. Ninth Australian Conference on Neural Networks, pp 236-240, 1998. University of Queensland.
M. Gallagher and T. Downs. Weight space learning trajectory visualization. In M. Dale et al., editors, Proc. Eighth Australian Conference on Neural Networks, pp 55-59, 1997. Telstra Research Laboratories.
M. Gallagher and T. Downs. Visualization of learning in neural networks using principal components analysis. In B. Verma and X. Yao, editors, Proc. International Conference on Computational Intelligence and Multimedia Applications, pp 327-331, 1997. Griffith University.

Other

M. Gallagher, J. Hogan and F. Maire (editors). Proceedings of the Sixth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2005). Lecture Notes in Computer Science vol.3578, 2005.
M. Gallagher and M. Frean. Population-Based Continuous Optimization and Probabilistic Modelling. Technical Report No. MG-1-2001, School of Information Technology and Electrical Engineering, University of Queensland. 2001.
M. Gallagher. Multi-layer Perceptron Error Surfaces: Visualization, Structure and Modelling. PhD thesis, Dept. Computer Science and Electrical Engineering, University of Queensland, 2000.
T. Downs, M. Frean and M. Gallagher (editors). Proceedings of the Ninth Australian Conference on Neural Networks (ACNN'98), 1998. University of Queensland.

Last modified: 27/09/11.