Professor Ender Ozcan's Outputs (3)

Recent advances in selection hyper-heuristics (2019)
Journal Article
Drake, J. H., Kheiri, A., Özcan, E., & Burke, E. K. (2020). Recent advances in selection hyper-heuristics. European Journal of Operational Research, 285(2), 405-428. https://doi.org/10.1016/j.ejor.2019.07.073

Hyper-heuristics have emerged as a way to raise the level of generality of search techniques for computational search problems. This is in contrast to many approaches, which represent customised methods for a single problem domain or a narrow class o... Read More about Recent advances in selection hyper-heuristics.

Fuzzy Hot Spot Identification for Big Data: An Initial Approach (2019)
Presentation / Conference Contribution
Triguero, I., Tickle, R., Figueredo, G. P., Mesgarpour, M., Ozcan, E., & John, R. I. (2019, June). Fuzzy Hot Spot Identification for Big Data: An Initial Approach. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Hot spot identification problems are present across a wide range of areas, such as transportation, health care and energy. Hot spots are locations where a certain type of event occurs with high frequency. A recent big data approach is capable of iden... Read More about Fuzzy Hot Spot Identification for Big Data: An Initial Approach.

A review on the self and dual interactions between machine learning and optimisation (2019)
Journal Article
Song, H., Triguero, I., & Özcan, E. (2019). A review on the self and dual interactions between machine learning and optimisation. Progress in Artificial Intelligence, 8(2), 143–165. https://doi.org/10.1007/s13748-019-00185-z

Machine learning and optimisation are two growing fields of artificial intelligence with an enormous number of computer science applications. The techniques in the former area aim to learn knowledge from data or experience, while the techniques from... Read More about A review on the self and dual interactions between machine learning and optimisation.