All Outputs (29)

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.

Evolutionary Approaches to Improving the Layouts of Instance-Spaces (2022)
Conference Proceeding
Sim, K., & Hart, E. (2022). Evolutionary Approaches to Improving the Layouts of Instance-Spaces. In Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022 (207-219). https://doi.org/10.1007/978-3-031-14714-2_15

We propose two new methods for evolving the layout of an instance-space. Specifically we design three different fitness metrics that seek to: (i) reward layouts which place instances won by the same solver close in the space; (ii) reward layouts that... Read More about Evolutionary Approaches to Improving the Layouts of Instance-Spaces.

Minimising line segments in linear diagrams is NP-hard (2022)
Journal Article
Chapman, P., Sim, K., & Hao Chen, H. (2022). Minimising line segments in linear diagrams is NP-hard. Journal of Computer Languages, 71, Article 101136. https://doi.org/10.1016/j.cola.2022.101136

Linear diagrams have been shown to be an effective method of representing set-based data. Moreover, a number of guidelines have been proven to improve the efficacy of linear diagrams. One of these guidelines is to minimise the number of line segments... Read More about Minimising line segments in linear diagrams is NP-hard.

A Neural Approach to Generation of Constructive Heuristics (2021)
Conference Proceeding
Alissa, M., Sim, K., & Hart, E. (2021). A Neural Approach to Generation of Constructive Heuristics. In 2021 IEEE Congress on Evolutionary Computation (CEC) (1147-1154). https://doi.org/10.1109/CEC45853.2021.9504989

Both algorithm-selection methods and hyper-heuristic methods rely on a pool of complementary heuristics. Improving the pool with new heuristics can improve performance, however, designing new heuristics can be challenging. Methods such as genetic pro... Read More about A Neural Approach to Generation of Constructive Heuristics.

Drawing Algorithms For Linear Diagrams (Supplementary) (2020)
Dataset
Chapman, P., & Sim, K. (2021). Drawing Algorithms For Linear Diagrams (Supplementary). [Dataset]. 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).

A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains (2020)
Conference Proceeding
Alissa, M., Sim, K., & Hart, E. (2020). A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains. . https://doi.org/10.1145/3377930.3390224

In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packing or scheduling. Typical approaches involve training a model to predict th... Read More about A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains.

Algorithm selection using deep learning without feature extraction (2019)
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 contrast to the majority of work in algorithm-selection, the approach does... Read More about Algorithm selection using deep learning without feature extraction.

Applications of Evolutionary Computation (2018)
Conference Proceeding
(2018). Applications of Evolutionary Computation. In K. Sim, & P. Kaufmann (Eds.), Applications of Evolutionary Computation. https://doi.org/10.1007/978-3-319-77538-8

This book constitutes the refereed conference proceedings of the 21st International Conference on the Applications of Evolutionary Computation, EvoApplications 2018, held in Parma, Italy, in April 2018, collocated with the Evo* 2018 events EuroGP, Ev... Read More about Applications of Evolutionary Computation.

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)
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 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.

Applications of Evolutionary Computation (2017)
Conference Proceeding
(2017). Applications of Evolutionary Computation. In G. Squillero, & K. Sim (Eds.), Applications of Evolutionary Computation (Part II). https://doi.org/10.1007/978-3-319-55792-2

The two volumes LNCS 10199 and 10200 constitute the refereed conference proceedings of the 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, held in Amsterdam, The Netherlands, in April 2017, colocated wi... Read More about Applications of Evolutionary Computation.

Applications of Evolutionary Computation (2017)
Conference Proceeding
(2017). Applications of Evolutionary Computation. In G. Squillero, & K. Sim (Eds.), Applications of Evolutionary Computation (Part I). https://doi.org/10.1007/978-3-319-55849-3

The two volumes LNCS 10199 and 10200 constitute the refereed conference proceedings of the 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, held in Amsterdam, The Netherlands, in April 2017, collocated w... Read More about Applications of Evolutionary Computation.

A hybrid method for feature construction and selection to improve wind-damage prediction in the forestry sector (2017)
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 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..

Genetic Programming (2015)
Conference Proceeding
Machado, P., Heywood, M. I., McDermott, J., Castelli, M., García-Sánchez, P., Burelli, P., …Sim, K. (2015). Genetic Programming. In Genetic Programming. https://doi.org/10.1007/978-3-319-16501-1

The 18th European Conference on Genetic Programming (EuroGP) took place during April 8–10, 2015. Copenhagen, Denmark was the setting, and the Nationalmuseet was the venue. EuroGP is the only conference exclusively devoted to the evolutionary gener... Read More about Genetic Programming.

A Novel Heuristic Generator for JSSP Using a Tree-Based Representation of Dispatching Rules (2015)
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 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.

Roll Project Bin Packing Benchmark Problems. (2015)
Dataset
Hart, E., & Sim, K. (2015). Roll Project Bin Packing Benchmark Problems. [Dataset]. https://doi.org/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 database files.

Roll Project Job Shop scheduling benchmark problems. (2015)
Dataset
Hart, E., & Sim, K. (2015). Roll Project Job Shop scheduling benchmark problems. [Dataset]. https://doi.org/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 containing a single CSV file for each problem instance using the format described... Read More about Roll Project Job Shop scheduling benchmark problems..

Roll Project Rich Vehicle Routing benchmark problems. (2015)
Dataset
Hart, E., & Sim, K. (2015). Roll Project Rich Vehicle Routing benchmark problems. [Dataset]. 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..