Real-valued Evolutionary Optimization using a Flexible Probability Density Estimator. - Marcus Gallagher; Marcus R. Frean; Tom Downs (GECCO, 1999)

Multi-layer perceptron error surfaces: visualization, structure and modelling - Marcus Gallagher (2000)

Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem. - Marcus Gallagher (ECML, 2001)

Empirical Evidence for Ultrametric Structure in Multi layer Perceptron Error Surfaces. - Marcus Gallagher; Tom Downs; Ian A. Wood (Neural Process. Lett., 2002)

Learning to play Pac-Man: an evolutionary, rule-based approach. - Marcus Gallagher; A. Ryan (IEEE Congress on Evolutionary Computation, 2003)

Playing in continuous spaces: some analysis and extension of population-based incremental learning. - Bo Yuan 003; Marcus Gallagher (IEEE Congress on Evolutionary Computation, 2003)

On building a principled framework for evaluating and testing evolutionary algorithms: a continuous landscape generator. - Bo Yuan 003; Marcus Gallagher (IEEE Congress on Evolutionary Computation, 2003)

Visualization of learning in multilayer perceptron networks using principal component analysis. - Marcus Gallagher; Tom Downs (IEEE Trans. Syst. Man Cybern. Part B, 2003)

Machine Learning for Matching Astronomy Catalogues. - David Rohde; Michael Drinkwater; Marcus Gallagher; Tom Downs; Marianne Doyle (IDEAL, 2004)

Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms. - Bo Yuan 003; Marcus Gallagher (PPSN, 2004)

A hybrid approach to parameter tuning in genetic algorithms. - Bo Yuan 003; Marcus Gallagher (Congress on Evolutionary Computation, 2005)

Experimental results for the special session on real-parameter optimization at CEC 2005: a simple, continuous EDA. - Bo Yuan 003; Marcus Gallagher (Congress on Evolutionary Computation, 2005)

On the importance of diversity maintenance in estimation of distribution algorithms. - Bo Yuan 003; Marcus Gallagher (GECCO, 2005)

MRI magnet design: search space analysis, EDAs and a real-world problem with significant dependencies. - Bo Yuan 003; Marcus Gallagher; Stuart Crozier (GECCO, 2005)

An Empirical Study of Hoeffding Racing for Model Selection in k-Nearest Neighbor Classification. - Flora Yu-Hui Yeh; Marcus Gallagher (IDEAL, 2005)

Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift. - Marcus Gallagher; Marcus R. Frean (Evol. Comput., 2005)

A Mathematical Modelling Technique for the Analysis of the Dynamics of a Simple Continuous EDA. - Bo Yuan 003; Marcus Gallagher (IEEE Congress on Evolutionary Computation, 2006)

A general-purpose tunable landscape generator. - Marcus Gallagher; Bo Yuan 003 (IEEE Trans. Evol. Comput., 2006)

Bayesian inference in estimation of distribution algorithms. - Marcus Gallagher; Ian A. Wood; Jonathan M. Keith; George Y. Sofronov (IEEE Congress on Evolutionary Computation, 2007)

A Comparison of Sequence Kernels for Localization Prediction of Transmembrane Proteins. - Stefan Maetschke; Marcus Gallagher; Mikael Bodén (CIBCB, 2007)

Evolving Pac-Man Players: Can We Learn from Raw Input? - Marcus Gallagher; Mark Ledwich (CIG, 2007)

An agent based approach to examining shared situation awareness. - Simon Connelly; Peter A. Lindsay; Marcus Gallagher (ICECCS, 2007)

Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks. - Bo Yuan 003; Marcus Gallagher (Parameter Setting in Evolutionary Algorithms, 2007)

Learning to be a Bot: Reinforcement Learning in Shooter Games. - Michelle McPartland; Marcus Gallagher (AIIDE, 2008)

Creating a multi-purpose first person shooter bot with reinforcement learning. - Michelle McPartland; Marcus Gallagher (CIG, 2008)

An influence map model for playing Ms. Pac-Man. - Nathan Wirth; Marcus Gallagher (CIG, 2008)

An empirical study of the sample size variability of optimal active learning using Gaussian process regression. - Flora Yu-Hui Yeh; Marcus Gallagher (IJCNN, 2008)

Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noiseless function testbed. - Marcus Gallagher (GECCO (Companion), 2009)

Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noisy function testbed. - Marcus R. Gallagher (GECCO (Companion), 2009)

Convergence analysis of UMDAC with finite populations: a case study on flat landscapes. - Bo Yuan 003; Marcus Gallagher (GECCO, 2009)

An improved small-sample statistical test for comparing the success rates of evolutionary algorithms. - Bo Yuan 003; Marcus Gallagher (GECCO, 2009)

Unsupervised DRG Upcoding Detection in Healthcare Databases. - Wei Luo 001; Marcus Gallagher (ICDM Workshops, 2010)

When Does Dependency Modelling Help? Using a Randomized Landscape Generator to Compare Algorithms in Terms of Problem Structure. - Rachael Morgan; Marcus Gallagher (PPSN (1), 2010)

Using Gaussian Process with Test Rejection to Detect T-Cell Epitopes in Pathogen Genomes. - Liwen You; Vladimir Brusic; Marcus Gallagher; Mikael Bodén (IEEE ACM Trans. Comput. Biol. Bioinform., 2010)

Faster and Parameter-Free Discord Search in Quasi-Periodic Time Series. - Wei Luo 001; Marcus Gallagher (PAKDD (2), 2011)

Reinforcement Learning in First Person Shooter Games. - Michelle McPartland; Marcus Gallagher (IEEE Trans. Comput. Intell. AI Games, 2011)

Game Designers Training First Person Shooter Bots. - Michelle McPartland; Marcus Gallagher (Australasian Conference on Artificial Intelligence, 2012)

Variable screening for reduced dependency modelling in Gaussian-based continuous Estimation of Distribution Algorithms. - Krishna Manjari Mishra; Marcus Gallagher (IEEE Congress on Evolutionary Computation, 2012)

Interactively training first person shooter bots. - Michelle McPartland; Marcus Gallagher (CIG, 2012)

Beware the Parameters: Estimation of Distribution Algorithms Applied to Circles in a Square Packing. - Marcus Gallagher (PPSN (2), 2012)

Length Scale for Characterising Continuous Optimization Problems. - Rachael Morgan; Marcus Gallagher (PPSN (1), 2012)

Using Landscape Topology to Compare Continuous Metaheuristics: A Framework and Case Study on EDAs and Ridge Structure. - R. Morgan; Marcus Gallagher (Evol. Comput., 2012)

Introducing Cloud Computing Topics in Curricula. - Ling Chen 004; Yang Liu; Marcus Gallagher; Bernard Pailthorpe; Shazia Sadiq; Heng Tao Shen; Xue Li 001 (J. Inf. Syst. Educ., 2012)

The turing test track of the 2012 Mario AI Championship: Entries and evaluation. - Noor Shaker; Julian Togelius; Georgios N. Yannakakis; Likith Poovanna; Vinay Sudha Ethiraj; Stefan J. Johansson; Robert G. Reynolds; Leonard Kinnaird-Heether; Tom Schumann; Marcus Gallagher (CIG, 2013)

Parameter-Free Search of Time-Series Discord. - Wei Luo 001; Marcus Gallagher; Janet Wiles (J. Comput. Sci. Technol., 2013)

Clustering Problems for More Useful Benchmarking of Optimization Algorithms. - Marcus Gallagher (SEAL, 2014)

A Modified Screening Estimation of Distribution Algorithm for Large-Scale Continuous Optimization. - Krishna Manjari Mishra; Marcus Gallagher (SEAL, 2014)

Fitness Landscape Analysis of Circles in a Square Packing Problems. - Rachael Morgan; Marcus Gallagher (SEAL, 2014)

Detecting contaminated birthdates using generalized additive models. - Wei Luo 001; Marcus Gallagher; Bill Loveday; Susan Ballantyne; Jason P. Connor; Janet Wiles (BMC Bioinform., 2014)

Sampling Techniques and Distance Metrics in High Dimensional Continuous Landscape Analysis: Limitations and Improvements. - Rachael Morgan; Marcus Gallagher (IEEE Trans. Evol. Comput., 2014)

Detecting Anomalies in Controlled Drug Prescription Data Using Probabilistic Models. - Xuelei Hu; Marcus Gallagher; William Loveday; Jason P. Connor; Janet Wiles (ACALCI, 2015)

A Stochastic Process Model of Classical Search. - Dimitri Klimenko; Hanna Kurniawati; Marcus Gallagher (CoRR, 2015)

Towards improved benchmarking of black-box optimization algorithms using clustering problems. - Marcus Gallagher (Soft Comput., 2016)

Exploratory Analysis of Clustering Problems Using a Comparison of Particle Swarm Optimization and Differential Evolution. - Sobia Saleem; Marcus Gallagher (ACALCI, 2017)

Parallel evolutionary algorithm for single and multi-objective optimisation: Differential evolution and constraints handling. - Dorival M. Pedroso; Mohammad Reza Bonyadi; Marcus Gallagher (Appl. Soft Comput., 2017)

Use of freely available datasets and machine learning methods in predicting deforestation. - Helen Mayfield; Carl S. Smith; Marcus Gallagher; Marc Hockings (Environ. Model. Softw., 2017)

Analysing and characterising optimization problems using length scale. - Rachael Morgan; Marcus Gallagher (Soft Comput., 2017)

Intra-task Curriculum Learning for Faster Reinforcement Learning in Video Games. - Nathaniel du Preez-Wilkinson; Marcus Gallagher; Xuelei Hu (Australasian Conference on Artificial Intelligence, 2018)

Flood-Fill Q-Learning Updates for Learning Redundant Policies in Order to Interact with a Computer Screen by Clicking. - Nathaniel du Preez-Wilkinson; Marcus Gallagher; Xuelei Hu (Australasian Conference on Artificial Intelligence, 2018)

Invariance of Weight Distributions in Rectified MLPs. - Russell Tsuchida; Farbod Roosta-Khorasani; Marcus Gallagher (ICML, 2018)

Workshops at PPSN 2018. - Robin C. Purshouse; Christine Zarges; Sylvain Cussat-Blanc; Michael G. Epitropakis; Marcus Gallagher; Thomas Jansen 001; Pascal Kerschke; Xiaodong Li 001; Fernando G. Lobo; Julian F. Miller; Pietro S. Oliveto; Mike Preuss; et al. (PPSN (2), 2018)

A Model-Based Framework for Black-Box Problem Comparison Using Gaussian Processes. - Sobia Saleem; Marcus Gallagher; Ian A. Wood (PPSN (2), 2018)

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. - Peter A. N. Bosman; Marcus Gallagher (Soft Comput., 2018)

Reversible Jump Probabilistic Programming. - David A. Roberts; Marcus Gallagher; Thomas Taimre (AISTATS, 2019)

Fitness Landscape Analysis in Data-Driven Optimization: An Investigation of Clustering Problems. - Marcus Gallagher (CEC, 2019)

Exploring the MLDA benchmark on the nevergrad platform. - Jérémy Rapin; Marcus Gallagher; Pascal Kerschke; Mike Preuss; Olivier Teytaud (GECCO (Companion), 2019)

Exchangeability and Kernel Invariance in Trained MLPs. - Russell Tsuchida; Fred (Farbod) Roosta; Marcus Gallagher (IJCAI, 2019)

Richer priors for infinitely wide multi-layer perceptrons. - Russell Tsuchida; Fred Roosta; Marcus Gallagher (CoRR, 2019)

Direct Feature Evaluation in Black-Box Optimization Using Problem Transformations. - Sobia Saleem; Marcus Gallagher; Ian A. Wood (Evol. Comput., 2019)

Quantitative measure of nonconvexity for black-box continuous functions. - Kenichi Tamura; Marcus Gallagher (Inf. Sci., 2019)

An Implementation and Experimental Evaluation of a Modularity Explicit Encoding Method for Neuroevolution on Complex Learning Tasks. - Yukai Qiao; Marcus Gallagher (Australasian Conference on Artificial Intelligence, 2020)

A Novel Mutation Operator for Variable Length Algorithms. - Saskia Van Ryt; Marcus Gallagher; Ian A. Wood (Australasian Conference on Artificial Intelligence, 2020)

Optimality-Based Analysis of XCSF Compaction in Discrete Reinforcement Learning. - Jordan T. Bishop; Marcus Gallagher (PPSN (2), 2020)

Fitness Landscape Features and Reward Shaping in Reinforcement Learning Policy Spaces. - Nathaniel du Preez-Wilkinson; Marcus Gallagher (PPSN (2), 2020)

Average-reward model-free reinforcement learning: a systematic review and literature mapping. - Vektor Dewanto; George Dunn; Ali Eshragh; Marcus Gallagher; Fred Roosta (CoRR, 2020)

Considerations for selecting a machine learning technique for predicting deforestation. - Helen Mayfield; Carl S. Smith; Marcus Gallagher; Marc Hockings (Environ. Model. Softw., 2020)

Network Analysis and Visualisation of Opioid Prescribing Data. - Xuelei Hu; Marcus Gallagher; William Loveday; Abhilash Dev; Jason P. Connor (IEEE J. Biomed. Health Informatics, 2020)

Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks. - Russell Tsuchida; Tim Pearce; Christopher van der Heide; Fred Roosta; Marcus Gallagher (AAAI, 2021)

A genetic fuzzy system for interpretable and parsimonious reinforcement learning policies. - Jordan T. Bishop; Marcus Gallagher; Will N. Browne (GECCO Companion, 2021)

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System. - Wai Weng Lo; Siamak Layeghy; Mohanad Sarhan; Marcus Gallagher; Marius Portmann (CoRR, 2021)

Benchmarking the Benchmark - Analysis of Synthetic NIDS Datasets. - Siamak Layeghy; Marcus Gallagher; Marius Portmann (CoRR, 2021)

A nearly Blackwell-optimal policy gradient method. - Vektor Dewanto; Marcus Gallagher (CoRR, 2021)

Examining Average and Discounted Reward Optimality Criteria in Reinforcement Learning. - Vektor Dewanto; Marcus Gallagher (AI, 2022)

Graph Neural Network-based Android Malware Classification with Jumping Knowledge. - Wai Weng Lo; Siamak Layeghy; Mohanad Sarhan; Marcus Gallagher; Marius Portmann (DSC, 2022)

Pittsburgh learning classifier systems for explainable reinforcement learning: comparing with XCS. - Jordan T. Bishop; Marcus Gallagher; Will N. Browne (GECCO, 2022)

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT. - Wai Weng Lo; Siamak Layeghy; Mohanad Sarhan; Marcus Gallagher; Marius Portmann (NOMS, 2022)

Approximate discounting-free policy evaluation from transient and recurrent states. - Vektor Dewanto; Marcus Gallagher (CoRR, 2022)

Modularity in NEAT Reinforcement Learning Networks. - Humphrey Munn; Marcus Gallagher (CoRR, 2022)

Using regression models for characterizing and comparing black box optimization problems. - Sobia Saleem; Marcus Gallagher (Swarm Evol. Comput., 2022)

An Agile New Research Framework for Hybrid Human-AI Teaming: Trust, Transparency, and Transferability. - Sabrina B. Caldwell; Penny Sweetser; Nicholas O'Donnell; Matthew James Knight; Matthew Aitchison; Tom Gedeon; Daniel Johnson 001; Margot Brereton; Marcus Gallagher; David Conroy (ACM Trans. Interact. Intell. Syst., 2022)

Modularity Based Linkage Model For Neuroevolution. - Yukai Qiao; Marcus Gallagher (GECCO Companion, 2023)

Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement Learning. - Humphrey Munn; Marcus Gallagher (IJCNN, 2023)

From zero-shot machine learning to zero-day attack detection. - Mohanad Sarhan; Siamak Layeghy; Marcus Gallagher; Marius Portmann (Int. J. Inf. Sec., 2023)

Guest Editorial: Special Issue on Evolutionary Computation for Games. - Jacob Schrum; Jialin Liu 001; Cameron Browne; Anikó Ekárt; Marcus Gallagher (IEEE Trans. Games, 2023)

Towards an Improved Understanding of Features for More Interpretable Landscape Analysis. - Marcus Gallagher; Mario A. Muñoz (GECCO Companion, 2024)

Searching for Benchmark Problem Instances from Data-Driven Optimisation. - Sara Hajari; Marcus Gallagher (GECCO Companion, 2024)

Analyzing the Runtime of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) on the Concatenated Trap Function. - Yukai Qiao; Marcus Gallagher (GECCO Companion, 2024)

Whole-Body Dynamic Throwing with Legged Manipulators. - Humphrey Munn; Brendan Tidd; David Howard; Marcus Gallagher (CoRR, 2024)

Feature extraction for machine learning-based intrusion detection in IoT networks. - Mohanad Sarhan; Siamak Layeghy; Nour Moustafa; Marcus Gallagher; Marius Portmann (Digit. Commun. Networks, 2024)

Benchmarking the benchmark - Comparing synthetic and real-world Network IDS datasets. - Siamak Layeghy; Marcus Gallagher; Marius Portmann (J. Inf. Secur. Appl., 2024)

An empirical evaluation of preprocessing methods for machine learning based network intrusion detection systems. - Liam Daly Manocchio; Siamak Layeghy; Marcus Gallagher; Marius Portmann (Eng. Appl. Artif. Intell., 2025)

Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning. - Llewyn Salt; Marcus Gallagher (CoRR, 2025)

AlgOS: Algorithm Operating System. - Llewyn Salt; Marcus Gallagher (CoRR, 2025)

Hyperparameter Optimisation with Practical Interpretability and Explanation Methods in Probabilistic Curriculum Learning. - Llewyn Salt; Marcus Gallagher (CoRR, 2025)

A Standardized Benchmark Set of Clustering Problem Instances for Comparing Black-Box Optimizers. - Diederick Vermetten; Catalin-Viorel Dinu; Marcus Gallagher (CoRR, 2025)