Comparing Performance of the Learnable Evolution Model and Genetic Algorithms. - Mark Coletti; Thomas D. Lash; Ryszard S. Michalski; Craig Mandsager; Rida E. Moustafa (GECCO, 1999)

A preliminary study of learnable evolution methodology implemented with C4.5. - Mark Coletti (IEEE Congress on Evolutionary Computation, 2002)

The relationship between evolvability and bloat. - Jeffrey K. Bassett; Mark Coletti; Kenneth A. De Jong (GECCO, 2009)

Learnable evolution model performance impaired by binary tournament survival selection. - Mark Coletti (GECCO (Companion), 2009)

Assessing the Role of Metacognition in GMU BICA. - Michael Q. Kalish; Alexei V. Samsonovich; Mark Coletti; Kenneth A. De Jong (BICA, 2010)

The effects of training set size and keeping rules on the emergent selection pressure of learnable evolution model. - Mark Coletti (GECCO (Companion), 2012)

Analysis of Emergent Selection Pressure in Evolutionary Algorithm and Machine Learner Offspring Filtering Hybrids. - Mark Coletti; Guido Cervone (SEMCCO, 2012)

Towards Validating a Model of Households and Societies in East Africa. - William G. Kennedy; Chenna Reddy Cotla; Tim Gulden; Mark Coletti; Claudio Cioffi-Revilla (WCSS, 2012)

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 (DLS@SC, 2019)

Performance analysis and optimization for scalable deployment of deep learning models for country-scale settlement mapping on Titan supercomputer. - Kuldeep R. Kurte; Jibonananda Sanyal; Anne Berres; Dalton D. Lunga; Mark Coletti; Hsiuhan Lexie Yang; Daniel Graves; Benjamin Liebersohn; Amy N. Rose (Concurr. Comput. Pract. Exp., 2019)

Library for evolutionary algorithms in Python (LEAP). - Mark Coletti; Eric O. Scott; Jeffrey K. Bassett (GECCO Companion, 2020)

Troubleshooting deep-learner training data problems using an evolutionary algorithm on Summit. - Mark Coletti; Alex Fafard; David Page (IBM J. Res. Dev., 2020)

Global Partitioning Elevation Normalization Applied to Building Footprint Prediction. - Alexander J. Fafard; Jan van Aardt; Mark Coletti; David L. Page (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2020)

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 (CEC, 2021)

Diagnosing autonomous vehicle driving criteria with an adversarial evolutionary algorithm. - Mark A. Coletti; Shang Gao 008; Spencer Paulissen; Nicholas Quentin Haas; Robert M. Patton (GECCO Companion, 2021)

Quantitative Evaluation of Autonomous Driving in CARLA. - Shang Gao 008; Spencer Paulissen; Mark Coletti; Robert M. Patton (IV Workshops, 2021)

Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. - Pravallika Devineni; Panchapakesan Ganesh; Nikhil Sivadas; Abhijeet Dhakane; Ketan Maheshwari; Drahomira Herrmannova; Ramakrishnan Kannan; Seung-Hwan Lim; Thomas E. Potok; Jordan B. Chipka; Priyantha Mudalige; Mark Coletti; et al. (SMC, 2021)

Avoiding Excess Computation in Asynchronous Evolutionary Algorithms. - Eric O. Scott; Mark Coletti; Catherine D. Schuman; Bill Kay; Shruti R. Kulkarni; Maryam Parsa; Kenneth A. De Jong (UKCI, 2021)

Training reinforcement learning models via an adversarial evolutionary algorithm. - Mark Coletti; Chathika Gunaratne; Catherine D. Schuman; Robert M. Patton (ICPP Workshops, 2022)

Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer. - Mu Gao; Mark Coletti; Russell B. Davidson; Ryan Prout; Subil Abraham; Benjamín Hernández; Ada Sedova (IPDPS Workshops, 2022)

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 (RSDHA@SC, 2022)

Research Software Engineering at Oak Ridge National Laboratory. - Addi Malviya-Thakur; David E. Bernholdt; William F. Godoy; Gregory R. Watson; Mathieu Doucet; Mark A. Coletti; David M. Rogers 001; Marshall T. McDonnell; Jay Jay Billings; Barney Maccabe (Comput. Sci. Eng., 2022)

SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing. - Prasanna Date; Chathika Gunaratne; Shruti R. Kulkarni; Robert M. Patton; Mark Coletti; Thomas E. Potok (ICONS, 2023)

Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II. - Mark Coletti; Ada Sedova; Rajni Chahal; Luke Gibson; Santanu Roy; Vyacheslav Bryantsev (ICPP Workshops, 2023)

Avoiding excess computation in asynchronous evolutionary algorithms. - Eric O. Scott; Mark Coletti; Catherine D. Schuman; Bill Kay; Shruti R. Kulkarni; Maryam Parsa; Chathika Gunaratne; Kenneth A. De Jong (Expert Syst. J. Knowl. Eng., 2023)

Predicted structural proteome of Sphagnum divinum and proteome-scale annotation. - Russell B. Davidson; Mark Coletti; Mu Gao; Bryan Piatkowski; Avinash Sreedasyam; Farhan Quadir; David J. Weston; Jeremy Schmutz; Jianlin Cheng; Jeffrey Skolnick; Jerry M. Parks; Ada Sedova (Bioinform., 2023)

Workflow Provenance in the Computing Continuum for Responsible, Trustworthy, and Energy-Efficient AI. - Renan Souza 001; Silvina Caíno-Lores; Mark Coletti; Tyler J. Skluzacek; Alexandru Costan; Frédéric Suter; Marta Mattoso; Rafael Ferreira da Silva (e-Science, 2024)

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 (GECCO Companion, 2024)

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 (SC Workshops, 2024)

Towards Cross-Facility Workflows Orchestration through Distributed Automation. - Tyler J. Skluzacek; Renan Souza 001; Mark Coletti; Frédéric Suter; Rafael Ferreira da Silva (PEARC, 2024)