Life Long Learning Hyper Heuristic Optimisation

Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn (2022)
Book Chapter
Hart, E. (2022). Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn. In A. E. Smith (Ed.), Women in Computational Intelligence: Key Advances and Perspectives on Emerging Topics (187-203). Springer. https://doi.org/10.1007/978-3-030-79092-9_9

Standard approaches to developing optimisation algorithms tend to involve selecting an algorithm and tuning it to work well on a large set of problem instances from the domain of interest. Once deployed, the algorithm remains static, failing to impro... Read More about Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn.

Use of machine learning techniques to model wind damage to forests (2018)
Journal Article
Hart, E., Sim, K., Kamimura, K., Meredieu, C., Guyon, D., & Gardiner, B. (2019). Use of machine learning techniques to model wind damage to forests. Agricultural and forest meteorology, 265, 16-29. https://doi.org/10.1016/j.agrformet.2018.10.022

This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed i... Read More about Use of machine learning techniques to model wind damage to forests.

Roll Project Rich Vehicle Routing benchmark problems. (2015)
Data
Hart, E., & Sim, K. (2015). Roll Project Rich Vehicle Routing benchmark problems. [Data]. https://doi.org/10.17869/ENU.2015.9367

This document describes a large set of Benchmark Problem Instances for the Rich Vehicle Routing Problem. All files are supplied as a single compressed (zipped) archive containing the instances, in XML format, an Object-Oriented Model supplied in XSD... Read More about Roll Project Rich Vehicle Routing benchmark problems..