| Object-oriented genetic improvement for improved energy consumption in Google Guava N Burles, E Bowles, AEI Brownlee, ZA Kocsis, J Swan, N Veerapen Search-Based Software Engineering: 7th International Symposium, SSBSE 2015 …, 2015 | 34 | 2015 |
| Templar – A Framework for Template-Method Hyper-Heuristics J Swan, N Burles Genetic Programming: 18th European Conference, EuroGP 2015, Copenhagen …, 2015 | 25 | 2015 |
| Search-based energy optimization of some ubiquitous algorithms AEI Brownlee, N Burles, J Swan IEEE Transactions on Emerging Topics in Computational Intelligence 1 (3 …, 2017 | 21 | 2017 |
| Embedded dynamic improvement N Burles, J Swan, E Bowles, AEI Brownlee, ZA Kocsis, N Veerapen Proceedings of the Companion Publication of the 2015 Annual Conference on …, 2015 | 12 | 2015 |
| Specialising Guava’s cache to reduce energy consumption N Burles, E Bowles, BR Bruce, K Srivisut Search-Based Software Engineering: 7th International Symposium, SSBSE 2015 …, 2015 | 8 | 2015 |
| A rule chaining architecture using a correlation matrix memory J Austin, S Hobson, N Burles, S O’Keefe Artificial Neural Networks and Machine Learning–ICANN 2012: 22nd …, 2012 | 6 | 2012 |
| ‘Quantum’ Parallel computation with neural networks N Burles Master’s thesis, University of York, 2010 | 5 | 2010 |
| Pattern recognition using associative memories NJ Burles | 4 | 2014 |
| ENAMeL: A language for binary correlation matrix memories: Reducing the memory constraints of matrix memories N Burles, S O’Keefe, J Austin, S Hobson Neural processing letters 40, 1-23, 2014 | 2 | 2014 |
| Extending the Associative Rule Chaining Architecture for Multiple Arity Rules N Burles, J Austin, S O’Keefe Neural-Symbolic Learning and Reasoning Workshop at IJCAI 2013, 2013 | 2 | 2013 |
| Improving the Associative Rule Chaining Architecture N Burles, S O’Keefe, J Austin Artificial Neural Networks and Machine Learning–ICANN 2013, 98-105, 2013 | 2 | 2013 |
| Full Implementation of an Estimation of Distribution Algorithm on a GPU S Poulding, J Staunton, N Burles CIGPU Competition, GECCO 2011, 2011 | 2 | 2011 |
| Incorporating scale invariance into the cellular associative neural network N Burles, S O’Keefe, J Austin Artificial Neural Networks and Machine Learning–ICANN 2014: 24th …, 2014 | 1 | 2014 |
| Datasets for the paper" Search-based energy optimization of some ubiquitous algorithms" AEI Brownlee, N Burles, J Swan University of Stirling. Faculty of Natural Sciences., 2017 | | 2017 |
| Hyper-quicksort: energy efficient sorting via the Templar framework for Template Method Hyper-heuristics J Swan, NJ Burles 39th CREST Open Workshop: Measuring, Testing and Optimising Computational …, 2015 | | 2015 |
| 2017 Index IEEE Transactions on Emerging Topics in Computational Intelligence Vol. HA Abbass, M Abouhawwash, N Agrawal, A Al-Mamun, C Alippi, V Bajaj, ... | | |
| Templar-template-method hyper-heuristics J Swan, N Burles | | |
Jerry SwanCEO, Hylomorph SolutionsVerified email at hylomorph-solutions.com
