| Benchmark datasets for the DNA fragment assembly problem GM Mallén-Fullerton, JA Hughes, S Houghten, G Fernández-Anaya International Journal of Bio-Inspired Computation 5 (6), 384-394, 2013 | 28 | 2013 |
| Models of parkinson's disease patient gait JA Hughes, S Houghten, JA Brown IEEE journal of biomedical and health informatics 24 (11), 3103-3110, 2019 | 18 | 2019 |
| Smartphone gait fingerprinting models via genetic programming JA Hughes, JA Brown, AM Khan 2016 IEEE Congress on Evolutionary Computation (CEC), 408-415, 2016 | 15 | 2016 |
| Recentering and restarting genetic algorithm variations for DNA fragment assembly J Hughes, S Houghten, GM Mallén-Fullerton, D Ashlock 2014 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2014 | 13 | 2014 |
| Restarting and recentering genetic algorithm variations for DNA fragment assembly: The necessity of a multi-strategy approach JA Hughes, S Houghten, D Ashlock Biosystems 150, 35-45, 2016 | 12 | 2016 |
| Recentering and restarting a genetic algorithm using a generative representation for an ordered gene problem JA Hughes, S Houghten, D Ashlock International journal of hybrid intelligent systems 11 (4), 257-271, 2014 | 12 | 2014 |
| Edit metric decoding: Representation strikes back J Hughes, JA Brown, S Houghten, D Ashlock 2013 IEEE Congress on Evolutionary Computation, 229-236, 2013 | 12 | 2013 |
| Vaccinating a population is a programming problem JA Hughes, M Dubé, S Houghten, D Ashlock 2020 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2020 | 10 | 2020 |
| Recentering, reanchoring & restarting an evolutionary algorithm J Hughes, S Houghten, D Ashlock 2013 World Congress on Nature and Biologically Inspired Computing, 76-83, 2013 | 10 | 2013 |
| On the generalizability of linear and non-linear region of interest-based multivariate regression models for fmri data EC Jackson, JA Hughes, M Daley 2018 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2018 | 9 | 2018 |
| Evolving the curve M Dubé, S Houghten, D Ashlock, JA Hughes 2020 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2020 | 8 | 2020 |
| Dietary intake of micronutrients are predictor of premenstrual syndrome, a machine learning method R Taheri, F ZareMehrjardi, N Heidarzadeh-Esfahani, JA Hughes, ... Clinical nutrition ESPEN 55, 136-143, 2023 | 6 | 2023 |
| Descriptive symbolic models of gaits from parkinson's disease patients JA Hughes, S Houghten, JA Brown 2019 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2019 | 6 | 2019 |
| Analysis of symbolic models of biometrie data and their use for action and user identification JA Hughes, JA Brown, AM Khan, AM Khattak, M Daley 2018 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2018 | 6 | 2018 |
| Finding nonlinear relationships in fmri time series with symbolic regression JA Hughes, M Daley Proceedings of the 2016 on Genetic and Evolutionary Computation Conference …, 2016 | 6 | 2016 |
| Vaccinating a population is a changing programming problem S Amin, S Houghten, JA Hughes 2021 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2021 | 5 | 2021 |
| Weighting on the world to change… an epidemic RV Jimenez, M Dubé, S Houghten, J Hughes 2021 IEEE Congress on Evolutionary Computation (CEC), 450-457, 2021 | 5 | 2021 |
| Modelling intracranial pressure with noninvasive physiological measures JA Hughes, EC Jackson, M Daley 2017 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2017 | 5 | 2017 |
| Searching for nonlinear relationships in fmri data with symbolic regression JA Hughes, M Daley Proceedings of the Genetic and Evolutionary Computation Conference, 1129-1136, 2017 | 5 | 2017 |
| Permutation problems, genetic algorithms, and dynamic representations JA Hughes, S Houghten, D Ashlock Nature-Inspired Computing and Optimization: Theory and Applications, 123-149, 2017 | 4 | 2017 |
Sheridan HoughtenBrock UniversityVerified email at brocku.ca
