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Dr. Nathan John Burles

(Former)

  1. 2017
  2. 2015
  3. Embedded Dynamic Improvement

    Burles, N. J., Swan, J., Bowles, E., Brownlee, A. E. I., Kocsis, Z. A. & Veerapen, N., 2015, GECCO Companion '15: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. New York: ACM, p. 831-832

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

  4. Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava

    Burles, N., Bowles, E., Brownlee, A. E. I., Kocsis, Z. A., Swan, J. & Veerapen, N., 2015, Search-Based Software Engineering: 7th International Symposium, SSBSE 2015, Bergamo, Italy, September 5-7, 2015, Proceedings. Springer, p. 255-261 7 p. (Lecture Notes in Computer Science; vol. 9275).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  5. Templar – A Framework for Template-Method Hyper-Heuristics

    Swan, J. & Burles, N. J., 2015, Genetic Programming: 18th European Conference, EuroGP 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings. Machado, P., Heywood, M., McDermott, J., Castelli, M., Garcia-Sanchez, P., Burelli, P., Risi, S. & Sim, K. (eds.). Cham: Springer International Publishing, p. 205-216 (Lecture Notes in Computer Science; vol. 9025).

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

  6. 2014
  7. Incorporating scale invariance into the cellular associative neural network

    Burles, N., O'Keefe, S. & Austin, J., 1 Jan 2014, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer-Verlag, Vol. 8681 LNCS. p. 435-442 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 8681 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

  8. Pattern Recognition Using Associative Memories

    Burles, N. J., 2014, 182 p.

    Research output: ThesisDoctoral Thesis

  9. 2013
  10. Extending the Associative Rule Chaining Architecture for Multiple Arity Rules

    Burles, N. J., Austin, J. & O'Keefe, S., 2013, Proceedings of the Ninth International Workshop on Neural-Symbolic Learning and Reasoning. p. 47-51 5 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  11. Improving the associative rule chaining architecture

    Burles, N. J., O'Keefe, S. & Austin, J., 2013, Artificial Neural Networks and Machine Learning - ICANN 2013: 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 2013. Proceedings.. Mladenov, V., Palm, G., Appollini, B., Koprinkova-Hristova, P., Villa, A. & Kasabov, N. (eds.). Berlin: Springer-Verlag, Vol. 8131 LNCS. p. 98-105 8 p. (Lecture Notes in Computer Science; vol. 8131).

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

  12. 2012
  13. A Rule Chaining Architecture Using a Correlation Matrix Memory

    Austin, J., Hobson, S. J., Burles, N. J. & O'Keefe, S., 2012, Artificial Neural Networks and Machine Learning – ICANN 2012: 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part I. PART 1 ed. SPRINGER-VERLAG BERLIN, Vol. 7552. p. 49-56 (Lecture Notes in Computer Science; vol. 7552).

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

  14. 2011
  15. Full Implementation of an Estimation of Distribution Algorithm on a GPU

    Poulding, S. M., Staunton, J. P. & Burles, N. J., 2011.

    Research output: Contribution to conferencePaper

  16. 2010
  17. ‘Quantum’ Parallel computation with neural networks

    Burles, N. J., 2010, 85 p.

    Research output: ThesisMaster's Thesis