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

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...

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...

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...

Roll Project Bin Packing Benchmark Problems.

Dataset
Hart, E. & Sim, K. (2015)
Roll Project Bin Packing Benchmark Problems. doi: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 d...

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...

Genetic Programming

Conference Proceeding
Machado, P., Heywood, M. I., McDermott, J., Castelli, M., García-Sánchez, P., Burelli, P., …Sim, K. (2015)
Genetic Programming. In Genetic Programminghttps://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. Eur...

Roll Project Rich Vehicle Routing benchmark problems.

Dataset
Hart, E. & Sim, K. (2015)
Roll Project Rich Vehicle Routing benchmark problems. doi: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 contain...

Roll Project Job Shop scheduling benchmark problems.

Dataset
Hart, E. & Sim, K. (2015)
Roll Project Job Shop scheduling benchmark problems. doi: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 contain...

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