| 2024 | GECCO | Efficacy of using a dynamic length representation vs. a fixed-length for neuroarchitecture search. | Mark Coletti, Chathika Gunaratne, Steven R. Young, Swetha Varadarajan, Robert M. Patton, Thomas E. Potok |
| 2024 | SC | Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications. | Sanjif Shanmugavelu, Mathieu Taillefumier, Christopher Culver, Oscar R. Hernandez, Mark Coletti, Ada Sedova |
| 2022 | SC | Neuromorphic Computing for Scientific Applications. | Robert M. Patton, Prasanna Date, Shruti R. Kulkarni, Chathika Gunaratne, Seung-Hwan Lim, Guojing Cong, Steven R. Young, Mark Coletti, Thomas E. Potok, Catherine D. Schuman |
| 2021 | CEC | Multi-Objective Hyperparameter Optimization for Spiking Neural Network Neuroevolution. | Maryam Parsa, Shruti R. Kulkarni, Mark Coletti, Jeffrey K. Bassett, J. Parker Mitchell, Catherine D. Schuman |
| 2020 | GECCO | Library for evolutionary algorithms in Python (LEAP). | Mark Coletti, Eric O. Scott, Jeffrey K. Bassett |
| 2019 | SC | Evolving Larger Convolutional Layer Kernel Sizes for a Settlement Detection Deep-Learner on Summit. | Mark Coletti, Dalton D. Lunga, Jeffrey K. Bassett, Amy N. Rose |
| 2012 | GECCO | The effects of training set size and keeping rules on the emergent selection pressure of learnable evolution model. | Mark Coletti |
| 2009 | GECCO | The relationship between evolvability and bloat. | Jeffrey K. Bassett, Mark Coletti, Kenneth A. De Jong |
| 2009 | GECCO | Learnable evolution model performance impaired by binary tournament survival selection. | Mark Coletti |
| 2002 | CEC | A preliminary study of learnable evolution methodology implemented with C4.5. | Mark Coletti |
| 1999 | GECCO | Comparing Performance of the Learnable Evolution Model and Genetic Algorithms. | Mark Coletti, Thomas D. Lash, Ryszard S. Michalski, Craig Mandsager, Rida E. Moustafa |