Outputs (11)

To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features (2023)
Presentation / Conference Contribution
Vermetten, D., Wang, H., Sim, K., & Hart, E. (2023, April). To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features. Presented at Evo Applications 2023, Brno, Czech Republic

Dynamic algorithm selection aims to exploit the complementarity of multiple optimization algorithms by switching between them during the search. While these kinds of dynamic algorithms have been shown to have potential to outperform their component a... Read More about To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features.

Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches (2023)
Journal Article
Alissa, M., Sim, K., & Hart, E. (2023). Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches. Journal of Heuristics, 29(1), 1-38. 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 online bin-packing. Specifically we train two types of recurrent neural netw... Read More about Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches.

Drawing Algorithms For Linear Diagrams (Supplementary) (2020)
Data
Chapman, P., & Sim, K. (2021). Drawing Algorithms For Linear Diagrams (Supplementary). [Data]. https://doi.org/10.17869/enu.2021.2748170

This folder contains the material to go with the article:

Peter Chapman, Kevin Sim, Huanghao Chen (2021) Drawing Algorithms for Linear Diagrams.

The code, the benchmark set of diagrams, the dataset of algorithms applied to the benchmark set, an... Read More about Drawing Algorithms For Linear Diagrams (Supplementary).

Algorithm selection using deep learning without feature extraction (2019)
Presentation / Conference Contribution
Alissa, M., Sim, K., & Hart, E. (2019, July). Algorithm selection using deep learning without feature extraction. Presented at Genetic and Evolutionary Computing Conference (GECCO) 2019, Prague, Czech Republic

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 contrast to the majority of work in algorithm-selection, the approach does... Read More about Algorithm selection using deep learning without feature extraction.

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.

A new rich vehicle routing problem model and benchmark resource (2018)
Presentation / Conference Contribution
Sim, K., Hart, E., Urquhart, N. B., & Pigden, T. (2015, September). A new rich vehicle routing problem model and benchmark resource. Presented at International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems, EUROGEN-2015, University of Strathclyde, Glasgow

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 characteristics. The model is used to generate 4800 new instances of rich VRPs which... Read More about A new rich vehicle routing problem model and benchmark resource.

A hybrid method for feature construction and selection to improve wind-damage prediction in the forestry sector (2017)
Presentation / Conference Contribution
Hart, E., Sim, K., Gardiner, B., & Kamimura, K. (2017, July). A hybrid method for feature construction and selection to improve wind-damage prediction in the forestry sector. Presented at Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '17

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 risk assessment methods is thus one of the keys to finding forest management... Read More about A hybrid method for feature construction and selection to improve wind-damage prediction in the forestry sector.

On Constructing Ensembles for Combinatorial Optimisation (2017)
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 algorithms have received relatively little attention. Existing approaches lag beh... Read More about On Constructing Ensembles for Combinatorial Optimisation.

A hyper-heuristic ensemble method for static job-shop scheduling. (2016)
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-conquer approach in which each heuristic solves a unique subset of the instance... Read More about A hyper-heuristic ensemble method for static job-shop scheduling..

A Novel Heuristic Generator for JSSP Using a Tree-Based Representation of Dispatching Rules (2015)
Presentation / Conference Contribution
Sim, K., & Hart, E. (2015, July). A Novel Heuristic Generator for JSSP Using a Tree-Based Representation of Dispatching Rules. Presented at Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15

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 that cooperate to cover the heuristic search space. A new heuristic generato... Read More about A Novel Heuristic Generator for JSSP Using a Tree-Based Representation of Dispatching Rules.

A research agenda for metaheuristic standardization. (2015)
Presentation / Conference Contribution
Hart, E., & Sim, K. (2015, June). A research agenda for metaheuristic standardization. Paper presented at 11th Metaheuristics International Conference

We propose that the development of standardized, explicit, machine-readable descriptions of metaheuris- tics will greatly advance scientific progress in the field. In particular, we advocate a purely functional description of metaheuristics — separat... Read More about A research agenda for metaheuristic standardization..