Use of machine learning techniques to model wind damage to forests
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 r...
Roll Project Bin Packing Benchmark Problems.
Dataset
Hart, E. & Sim, K. (2015)
Roll Project Bin Packing Benchmark Problems. doi:10.17869/ENU.2015.9364
This document describes two sets of Benchmark Problem Instances for the One Dimensional Bin Packing Problem. The problem instances are supplied as compressed (zipped) SQLITE d...
Roll Project Rich Vehicle Routing benchmark problems.
Dataset
Hart, E. & Sim, K. (2015)
Roll Project Rich Vehicle Routing benchmark problems. doi: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 contain...
Roll Project Job Shop scheduling benchmark problems.
Dataset
Hart, E. & Sim, K. (2015)
Roll Project Job Shop scheduling benchmark problems. doi:10.17869/ENU.2015.9365
This document describes two sets of benchmark problem instances for the job shop scheduling problem. Each set of instances is supplied as a compressed (zipped) archive contain...
Novel Hyper-heuristics Applied to the Domain of Bin Packing
Thesis
Sim, K. Novel Hyper-heuristics Applied to the Domain of Bin Packing. (Thesis)
Edinburgh Napier University. Retrieved from http://researchrepository.napier.ac.uk/id/eprint/7563
Principal to the ideology behind hyper-heuristic research is the desire to increase the level of generality of heuristic procedures so that they can be easily applied to a wid...