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...
A Novel Heuristic Generator for JSSP Using a Tree-Based Representation of Dispatching Rules
Conference Proceeding
Sim, K., & Hart, E. (2015)
A Novel Heuristic Generator for JSSP Using a Tree-Based Representation of Dispatching Rules. In GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, (1485-1486). https://doi.org/10.1145/2739482.2764697
A previously described hyper-heuristic framework named
NELLI is adapted for the classic Job Shop Scheduling Problem (JSSP) and used to find ensembles of reusable heuristics th...
A new rich vehicle routing problem model and benchmark resource
Conference Proceeding
Sim, K., Hart, E., Urquhart, N. B., & Pigden, T. (2018)
A new rich vehicle routing problem model and benchmark resource. In Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. https://doi.org/10.1007/978-3-319-89988-6_30
We describe a new rich VRP model that captures many real-world constraints, following a recently proposed taxonomy that addresses both scenario and problem physical characteri...
Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches
Journal Article
Alissa, M., Sim, K., & Hart, E. (in press)
Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches. Journal of Heuristics, https://doi.org/10.1007/s10732-022-09505-4
We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in o...
Algorithm selection using deep learning without feature extraction
Conference Proceeding
Alissa, M., Sim, K., & Hart, E. (2019)
Algorithm selection using deep learning without feature extraction. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. , (198-206). https://doi.org/10.1145/3321707.3321845
We propose a novel technique for algorithm-selection which adopts a deep-learning approach, specifically a Recurrent-Neural Network with Long-Short-Term-Memory (RNN-LSTM). In ...
A hybrid method for feature construction and selection to improve wind-damage prediction in the forestry sector
Conference Proceeding
Hart, E., Sim, K., Gardiner, B., & Kamimura, K. (2017)
A hybrid method for feature construction and selection to improve wind-damage prediction in the forestry sector. In GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference. , (1121-1128). https://doi.org/10.1145/3071178.3071217
Catastrophic damage to forests resulting from major storms has resulted in serious timber and financial losses within the sector across Europe in the recent past. Developing r...
A hyper-heuristic ensemble method for static job-shop scheduling.
Journal Article
Hart, E., & Sim, K. (2016)
A hyper-heuristic ensemble method for static job-shop scheduling. Evolutionary Computation, 24(4), 609-635. https://doi.org/10.1162/EVCO_a_00183
We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conq...
A research agenda for metaheuristic standardization.
Presentation / Conference
Hart, E., & Sim, K. (2015, June)
A research agenda for metaheuristic standardization. Paper presented at 11th Metaheuristics International Conference, Agadir, Morocco
We propose that the development of standardized, explicit, machine-readable descriptions of metaheuris- tics will greatly advance scientific progress in the field. In particul...
On Constructing Ensembles for Combinatorial Optimisation
Journal Article
Hart, E., & Sim, K. (2018)
On Constructing Ensembles for Combinatorial Optimisation. Evolutionary Computation, 26(1), 67-87. https://doi.org/10.1162/evco_a_00203
Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algori...