Caitlin Owen

2024

Conference Contribution - Published proceedings: Full paper

Dick, G., & Owen, C. A. (2024). Characterising the double descent of symbolic regression. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). (pp. 2050-2057). New York, NY: ACM. doi: 10.1145/3638530.3664176

2023

Journal - Research Article

Owen, C. A., Dick, G., & Whigham, P. A. (2023). Using decomposed error for reproducing implicit understanding of algorithms. Evolutionary Computation. Advance online publication. doi: 10.1162/evco_a_00321

2022

Journal - Research Article

Owen, C. A., Dick, G., & Whigham, P. A. (2022). Standardization and data augmentation in genetic programming. IEEE Transactions on Evolutionary Computation, 26(6), 1596-1608. doi: 10.1109/TEVC.2022.3160414

Owen, P. D., & Owen, C. A. (2022). Simulation evidence on Herfindahl-Hirschman measures of competitive balance in professional sports leagues. Journal of the Operational Research Society, 73(2), 285-300. doi: 10.1080/01605682.2020.1835449

Conference Contribution - Published proceedings: Full paper

Owen, C. A., Dick, G., & Whigham, P. A. (2022). Towards explainable AutoML using error decomposition. Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 13728). (pp. 177-190). Cham, Switzerland: Springer. doi: 10.1007/978-3-031-22695-3_32

2021

Awarded Doctoral Degree

Owen, C. A. (2021). Error decomposition of evolutionary machine learning (PhD). University of Otago, Dunedin, New Zealand. Retrieved from http://hdl.handle.net/10523/12234

2020

Journal - Research Article

Owen, C. A., Dick, G., & Whigham, P. A. (2020). Characterizing genetic programming error through extended bias and variance decomposition. IEEE Transactions on Evolutionary Computation, 24(6), 1164-1176. doi: 10.1109/tevc.2020.2990626

Meldrum, S., Licorish, S. A., Owen, C. A., & Savarimuthu, B. T. R. (2020). Understanding stack overflow code quality: A recommendation of caution. Science of Computer Programming, 199, 102516. doi: 10.1016/j.scico.2020.102516

Conference Contribution - Published proceedings: Full paper

Dick, G., Owen, C. A., & Whigham, P. A. (2020). Feature standardisation and coefficient optimisation for effective symbolic regression. Proceedings of the Genetic & Evolutionary Computation Conference (GECCO). (pp. 306-314). New York, NY: ACM. doi: 10.1145/3377930.3390237

2018

Conference Contribution - Published proceedings: Full paper

Owen, C. A., Dick, G., & Whigham, P. A. (2018). Feature standardisation in symbolic regression. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artifical intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 565-576). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_52

Dick, G., Owen, C. A., & Whigham, P. A. (2018). Evolving bagging ensembles using a spatially-structured niching method. Proceedings of the Genetic and Evolutionary Computation Conference. (pp. 418-425). New York, NY: ACM. doi: 10.1145/3205455.3205642

2017

Working Paper; Discussion Paper; Technical Report

Owen, P. D., & Owen, C. A. (2017). Simulation evidence on Herfindahl-Hirschman indices as measures of competitive balance [Economics Discussion Papers No. 1715]. Dunedin, New Zealand: University of Otago. Retrieved from http://hdl.handle.net/10523/7797

2015

Journal - Research Article

Whigham, P. A., Owen, C. A., & MacDonell, S. G. (2015). A baseline model for software effort estimation. ACM Transactions on Software Engineering & Methodology, 24(3), 20. doi: 10.1145/2738037

Conference Contribution - Published proceedings: Full paper

Whigham, P. A., Dick, G., Maclaurin, J., & Owen, C. A. (2015). Examining the "best of both worlds" of grammatical evolution. Proceedings of the Genetic and Evolutionary Computation (GECCO) Conference. (pp. 1111-1118). New York: ACM. doi: 10.1145/2739480.2754784

2014

Conference Contribution - Published proceedings: Full paper

Whigham, P. A., & Owen, C. (2014). Multi-objective optimisation, software effort estimation and linear models. In G. Dick, W. N. Browne, P. Whigham, M. Zhang, L. T. Bui, H. Ishibuchi, … K. Tang (Eds.), Simulated evolution and learning: Lecture notes in computer science (Vol. 8886). (pp. 263-273). Cham, Switerzland: Springer. doi: 10.1007/978-3-319-13563-2