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

A research agenda for metaheuristic standardization.

Presentation / Conference
Hart, E., & Sim, K. (2015, June)
A research agenda for metaheuristic standardization. Paper presented at 11th Metaheuristics International Conference, Agadir, Morocco
We propose that the development of standardized, explicit, machine-readable descriptions of metaheuris- tics will greatly advance scientific progress in the field. In particul...

A Lifelong Learning Hyper-heuristic Method for Bin Packing.

Journal Article
Hart, E., Sim, K., & Paechter, B. (2015)
A Lifelong Learning Hyper-heuristic Method for Bin Packing. Evolutionary Computation, 23(1), 37-67. https://doi.org/10.1162/EVCO_a_00121
We describe a novel Hyper-heuristic system which continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics ...

On the life-long learning capabilities of a NELLI*: a hyper-heuristic optimisation system.

Conference Proceeding
Hart, E., & Sim, K. (2014)
On the life-long learning capabilities of a NELLI*: a hyper-heuristic optimisation system. In Proceedings of PPSN, 13th International Conference on Parallel problem Solving from Nature, (282-291). https://doi.org/10.1007/978-3-319-10762-2_28
Real-world applications of optimisation techniques place more importance on finding approaches that result in acceptable quality solutions in a short time-frame and can provid...

Novel Hyper-heuristics Applied to the Domain of Bin Packing

Thesis
Sim, K. Novel Hyper-heuristics Applied to the Domain of Bin Packing. (Thesis)
Edinburgh Napier University. Retrieved from http://researchrepository.napier.ac.uk/id/eprint/7563
Principal to the ideology behind hyper-heuristic research is the desire to increase the level of generality of heuristic procedures so that they can be easily applied to a wid...

An improved immune inspired hyper-heuristic for combinatorial optimisation problems.

Conference Proceeding
Sim, K., & Hart, E. (2014)
An improved immune inspired hyper-heuristic for combinatorial optimisation problems. In C. Igel (Ed.), Proceedings of GECCO 2014 (Genetic and Evolutionary Computation Conference) (121-128). https://doi.org/10.1145/2576768.2598241
The meta-dynamics of an immune-inspired optimisation sys- tem NELLI are considered. NELLI has previously shown to exhibit good performance when applied to a large set of optim...

A real-world employee scheduling and routing application.

Conference Proceeding
Hart, E., Sim, K., & Urquhart, N. B. (2014)
A real-world employee scheduling and routing application. In C. Igel (Ed.), GECCO 2014 Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, (1239-1242). https://doi.org/10.1145/2598394.2605447
We describe a hyper-heuristic application developed for a client to find quick, acceptable solutions to Workforce Schedul- ing and Routing problems. An interactive fitness fun...

Learning to solve bin packing problems with an immune inspired hyper-heuristic.

Conference Proceeding
Sim, K., Hart, E., & Paechter, B. (2013)
Learning to solve bin packing problems with an immune inspired hyper-heuristic. In P. Liò, O. Miglino, G. Nicosia, S. Nolfi, & M. Pavone (Eds.), Advances in Artificial Life, ECAL 2013, 856-863. https://doi.org/10.7551/978-0-262-31709-2-ch126
Motivated by the natural immune system's ability to defend the body by generating and maintaining a repertoire of antibodies that collectively cover the potential pathogen spa...

Generating single and multiple cooperative heuristics for the one dimensional bin packing problem using a single node genetic programming island model.

Conference Proceeding
Sim, K., & Hart, E. (2013)
Generating single and multiple cooperative heuristics for the one dimensional bin packing problem using a single node genetic programming island model. In E. Alba (Ed.), Proceedgs of GECCO 2013, (1549-1556). https://doi.org/10.1145/2463372.2463555
Novel deterministic heuristics are generated using Single Node Genetic Programming for application to the One Dimensional Bin Packing Problem. First a single deterministic heu...

A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution.

Conference Proceeding
Sim, K., Hart, E., & Paechter, B. (2012)
A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution. In Parallel Problem Solving from Nature: PPSN XII, (348-357). https://doi.org/10.1007/978-3-642-32964-7_35
A hyper-heuristic for the one dimensional bin packing problem is presented that uses an Evolutionary Algorithm (EA) to evolve a set of attributes that characterise a problem i...

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