All Outputs (4)

Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities (2023)
Journal Article
Mansouri-Benssassi, E., Rogers, S., Reel, S., Malone, M., Smith, J., Ritchie, F., & Jefferson, E. (2023). Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities. Heliyon, 9(4), Article e15143. https://doi.org/10.1016/j.heliyon.2023.e15143

Introduction: Artificial intelligence (AI) applications in healthcare and medicine have increased in recent years. To enable access to personal data, Trusted Research Environments (TREs) (otherwise known as Safe Havens) provide safe and secure enviro... Read More about Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities.

From evolutionary computation to the evolution of things (2015)
Journal Article
Eiben, A. E., & Smith, J. (2015). From evolutionary computation to the evolution of things. Nature, 521(7553), 476-482. https://doi.org/10.1038/nature14544

© 2015 Macmillan Publishers Limited. All rights reserved . Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering... Read More about From evolutionary computation to the evolution of things.

Coevolving memetic algorithms: A review and progress report (2007)
Journal Article
Smith, J. (2007). Coevolving memetic algorithms: A review and progress report. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 37(1), 6-17. https://doi.org/10.1109/TSMCB.2006.883273

Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search (LS) is coadapted alongside candidate solutions within a hybrid evolutionary system. Simple versions of these systems h... Read More about Coevolving memetic algorithms: A review and progress report.

A tutorial for competent memetic algorithms: Model, taxonomy, and design issues (2005)
Journal Article
Krasnogor, N., & Smith, J. (2005). A tutorial for competent memetic algorithms: Model, taxonomy, and design issues. IEEE Transactions on Evolutionary Computation, 9(5), 474-488. https://doi.org/10.1109/TEVC.2005.850260

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learni... Read More about A tutorial for competent memetic algorithms: Model, taxonomy, and design issues.