| 2026 | GECCO | Audience-Customized Translation of Rule-Based Evidence with Large Language Models Across Multiplexer Benchmarks. | Harsh Bandhey, Gabriel Lipschutz-Villa, Khoi Dinh, Malek Kamoun, Ryan J. Urbanowicz |
| 2026 | GECCO | Phase-Alternation and Tree-Initialization to Facilitate Interpretable Rule-based Machine Learning. | Gabriel Lipschutz-Villa, Harsh Bandhey, Khoi Dinh, Michael Heider, Malek Kamoun, Ryan J. Urbanowicz |
| 2026 | GECCO | Introduction to Automated Machine Learning for Data Science, Modeling, and Benchmarking. | Ryan J. Urbanowicz |
| 2025 | GECCO | Rule-based Machine Learning: Separating Rule and Rule-Set Pareto-Optimization for Interpretable Noise-Agnostic Modeling. | Gabriel Lipschutz-Villa, Harsh Bandhey, Ruonan Yin, Malek Kamoun, Ryan J. Urbanowicz |
| 2025 | GECCO | Automated Machine Learning Tools for Data Science, Modeling, and Algorithm Benchmarking. | Ryan J. Urbanowicz |
| 2024 | GECCO | Evolutionary Machine Learning for Interpretable and eXplainable AI. | Abubakar Siddique, Will N. Browne, Ryan J. Urbanowicz |
| 2024 | GECCO | Survival-LCS: A Rule-Based Machine Learning Approach to Survival Analysis. | Alexa A. Woodward, Harsh Bandhey, Jason H. Moore, Ryan J. Urbanowicz |
| 2023 | GECCO | Modern Applications of Evolutionary Rule-based Machine Learning. | Abubakar Siddique, Will N. Browne, Ryan J. Urbanowicz |
| 2023 | GECCO | Scikit-FIBERS: An 'OR'-Rule Discovery Evolutionary Algorithm for Risk Stratification in Right-Censored Survival Analyses. | Ryan J. Urbanowicz, Harsh Bandhey, Malek Kamoun, Nolan Fogarty, Yi-An Hsieh |
| 2022 | AMIA | Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing. | Erin E. Kennedy, Anahita Davoudi, Sy Hwang, Ryan J. Urbanowicz, Philip J. Freda, Kathryn H. Bowles, Danielle L. Mowery |
| 2021 | GECCO | RARE: evolutionary feature engineering for rare-variant bin discovery. | Satvik Dasariraju, Ryan J. Urbanowicz |
| 2020 | GECCO | Evolutionary algorithms in biomedical data mining: challenges, solutions, and frontiers. | Ryan J. Urbanowicz, Moshe Sipper |
| 2020 | GECCO | Evolving genetic programming trees in a rule-based learning framework. | Siddharth Verma, Piyush Borole, Ryan J. Urbanowicz |
| 2020 | GECCO | A scikit-learn compatible learning classifier system. | Robert F. Zhang, Ryan J. Urbanowicz |
| 2019 | CEC | Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems. | Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz |
| 2019 | EUROGP | Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions. | Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz |
| 2018 | GECCO | Attribute tracking: strategies towards improved detection and characterization of complex associations. | Ryan J. Urbanowicz, Christopher Lo, John H. Holmes, Jason H. Moore |
| 2018 | GECCO | Introducing learning classifier systems: rules that capture complexity. | Ryan J. Urbanowicz, Danilo Vasconcellos Vargas |
| 2017 | GECCO | Introducing rule-based machine learning: capturing complexity. | Ryan J. Urbanowicz |
| 2016 | GECCO | Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. | Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, Jason H. Moore |
| 2016 | GECCO | Introducing Rule-Based Machine Learning: Capturing Complexity. | Ryan J. Urbanowicz |
| 2016 | GECCO | Hands-on Workshop on Learning Classifier Systems. | Ryan J. Urbanowicz, Will N. Browne, Karthik Kuber |
| 2016 | GECCO | Pareto Inspired Multi-objective Rule Fitness for Adaptive Rule-based Machine Learning. | Ryan J. Urbanowicz, Randal S. Olson, Jason H. Moore |
| 2016 | PPSN | Pareto Inspired Multi-objective Rule Fitness for Noise-Adaptive Rule-Based Machine Learning. | Ryan J. Urbanowicz, Randal S. Olson, Jason H. Moore |
| 2015 | GECCO | Introducing Rule-based Machine Learning: A Practical Guide. | Ryan J. Urbanowicz, Will N. Browne |
| 2015 | GECCO | Retooling Fitness for Noisy Problems in a Supervised Michigan-style Learning Classifier System. | Ryan J. Urbanowicz, Jason H. Moore |
| 2015 | GECCO | Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System. | Ryan J. Urbanowicz, Niranjan Ramanand, Jason H. Moore |
| 2014 | PPSN | An Extended Michigan-Style Learning Classifier System for Flexible Supervised Learning, Classification, and Data Mining. | Ryan J. Urbanowicz, Gediminas Bertasius, Jason H. Moore |
| 2013 | GECCO | Learning classifier systems: introducing the user-friendly textbook. | Will N. Browne, Ryan J. Urbanowicz |
| 2013 | GECCO | A simple multi-core parallelization strategy for learning classifier system evaluation. | James Rudd, Jason H. Moore, Ryan J. Urbanowicz |
| 2012 | GECCO | Instance-linked attribute tracking and feedback for michigan-style supervised learning classifier systems. | Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, Jason H. Moore |
| 2012 | PPSN | Using Expert Knowledge to Guide Covering and Mutation in a Michigan Style Learning Classifier System to Detect Epistasis and Heterogeneity. | Ryan J. Urbanowicz, Delaney Granizo-MacKenzie, Jason H. Moore |
| 2011 | GECCO | Random artificial incorporation of noise in a learning classifier system environment. | Ryan J. Urbanowicz, Nicholas A. Sinnott-Armstrong, Jason H. Moore |
| 2010 | GECCO | The application of michigan-style learning classifiersystems to address genetic heterogeneity and epistasisin association studies. | Ryan J. Urbanowicz, Jason H. Moore |
| 2010 | PPSN | The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies. | Ryan J. Urbanowicz, Jason H. Moore |
| 2008 | GECCO | Mask functions for the symbolic modeling of epistasis using genetic programming. | Ryan J. Urbanowicz, Nate Barney, Bill C. White, Jason H. Moore |