Dr Michael Mayo

Senior Lecturer (Computer Science)

Qualifications: PhD Cant BA Hons Otago

Contact Details

Room: G.2.20
Phone: +64 7 838 4403
Extension: 4403
Fax: +64 7 858 5095

Research Interests

Metaheuristic/evolutionary algorithms, AI, and machine learning, and applications in areas such as data mining and renewable energy.


Teaching Commitments

Recent Publications

  • Mayo, M. J., & Omranian, S. (2016). Towards a new evolutionary subsampling technique for heuristic optimisation of load disaggregators. In H. Cao, J. Li, & R. Wang (Eds.), Trends and Applications in Knowledge Discovery and Data Mining, PAKDD 2016 Workshops, Revised Selected Papers Vol. LNCS 9794 (pp. 3-14). Conference held at Auckland, NZ: Springer. doi:10.1007/978-3-319-42996-0_1 Open Access version: hdl:10289/10560

  • Mayo, M., & Bifet, A. (2016). Deferral classification of evolving temporal dependent data streams. In Proc 31st Annual ACM Symposium on Applied Computing (pp. 952-954). Pisa, Italy: ACM. doi:10.1145/2851613.2851890 Open Access version: hdl:10289/10549

  • Mayo, M., & Daoud, M. (2016). Informed mutation of wind farm layouts to maximise energy harvest. Renewable Energy, 89, 437-448. doi:10.1016/j.renene.2015.12.006 Open Access version: hdl:10289/10078

  • Mayo, M. J., Daoud, M., & Zheng, C. (2016). Randomising block sizes for BlockCopy-based wind farm layout optimisation. In G. Leu, H. K. Singh, & S. Elsayed (Eds.), Proc 20th Asia Pacific Symposium on Intelligent and Evolutionary Systems Vol. Proceedings in Adaptation, Learning and Optimization 8 (pp. 277-289). Canberra, Australia: Springer. doi:10.1007/978-3-319-49049-6_20 Open Access version: hdl:10289/10779

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