Kevin Sim
Kevin Sim

Dr Kevin Sim

Lecturer

 0131 455 2497

  K.Sim@napier.ac.uk

  

Biography

Dr. Kevin Sim gained a 1st Class Honours Degree in Software Technology in 2009 and an MSc in Advanced Software Engineering in 2010, both from from Edinburgh Napier University. His PhD, also from the Edinburgh Napier University (October 2014), explored the use of hyper-heuristics as a method of providing high quality solutions to optimisation problems


Before commencing his current employment as a lecturer in computing science he worked for 3 years as a research fellow working on an EPSRC funded project entitled Real World Optimisation with Life-Long Learning. His research interests lie in the field of biologically inspired computing and machine learning, with an emphasis on hyper-heuristics applied to real world problems including logistics, modelling and optimisation.

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Fellowships and Awards

  • IIDI 1st Year PhD student Kevin Sim won 1st Prize for the Best 1st Year Presentation at the Faculty for Engineering, Computing and Creative Industries Research Student Conference on Thursday 26th June

 

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29 results

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...

Evolutionary Approaches to Improving the Layouts of Instance-Spaces

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 in...

Minimising line segments in linear diagrams is NP-hard

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 d...

A Neural Approach to Generation of Constructive Heuristics

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, howeve...

Drawing Algorithms For Linear Diagrams (Supplementary)

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 ...

A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains

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 packi...

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 ...

Applications of Evolutionary Computation

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 P...

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 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...

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