Kevin Sim

Kevin Sim

Dr Kevin Sim

Lecturer

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.

News

Events

Esteem

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

 

Date


24 results

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

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. , (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 Computationhttps://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 Scienceshttps://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...

Applications of Evolutionary Computation

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, EvoApplica...

Applications of Evolutionary Computation

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, EvoApplica...

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...

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...

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...

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