Dr Kevin Sim's Outputs (33)

Genetic Programming (2015)
Presentation / Conference Contribution
Machado, P., Heywood, M. I., McDermott, J., Castelli, M., García-Sánchez, P., Burelli, P., Risi, S., & Sim, K. (2015, April). Genetic Programming

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)
Presentation / Conference Contribution
Sim, K., & Hart, E. (2015, July). A Novel Heuristic Generator for JSSP Using a Tree-Based Representation of Dispatching Rules. Presented at Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15

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 Job Shop scheduling benchmark problems. (2015)
Data
Hart, E., & Sim, K. (2015). Roll Project Job Shop scheduling benchmark problems. [Data]. 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)
Data
Hart, E., & Sim, K. (2015). Roll Project Rich Vehicle Routing benchmark problems. [Data]. 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..

A research agenda for metaheuristic standardization. (2015)
Presentation / Conference Contribution
Hart, E., & Sim, K. (2015, June). A research agenda for metaheuristic standardization. Paper presented at 11th Metaheuristics International Conference

We propose that the development of standardized, explicit, machine-readable descriptions of metaheuris- tics will greatly advance scientific progress in the field. In particular, we advocate a purely functional description of metaheuristics — separat... Read More about A research agenda for metaheuristic standardization..

A Lifelong Learning Hyper-heuristic Method for Bin Packing (2015)
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 and samples problems from its environment; representative problems and heur... Read More about A Lifelong Learning Hyper-heuristic Method for Bin Packing.

On the life-long learning capabilities of a NELLI*: a hyper-heuristic optimisation system. (2014)
Presentation / Conference Contribution
Hart, E., & Sim, K. (2014, September). On the life-long learning capabilities of a NELLI*: a hyper-heuristic optimisation system

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 provide robust solutions, capable of being modified in response to changes in the... Read More about On the life-long learning capabilities of a NELLI*: a hyper-heuristic optimisation system..

Novel Hyper-heuristics Applied to the Domain of Bin Packing (2014)
Thesis
Sim, K. Novel Hyper-heuristics Applied to the Domain of Bin Packing. (Thesis). Edinburgh Napier University. 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 wide variety of problems to produce solutions of adequate quality within pract... Read More about Novel Hyper-heuristics Applied to the Domain of Bin Packing.

A real-world employee scheduling and routing application. (2014)
Presentation / Conference Contribution
Hart, E., Sim, K., & Urquhart, N. B. (2014, July). A real-world employee scheduling and routing application. Presented at GECCO 2014

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 function controlled by the user enables five different objectives to be weight... Read More about A real-world employee scheduling and routing application..

An improved immune inspired hyper-heuristic for combinatorial optimisation problems. (2014)
Presentation / Conference Contribution
Sim, K., & Hart, E. (2014, July). An improved immune inspired hyper-heuristic for combinatorial optimisation problems

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 optimisation problems by sustaining a network of novel heuristics. We address th... Read More about An improved immune inspired hyper-heuristic for combinatorial optimisation problems..

Learning to solve bin packing problems with an immune inspired hyper-heuristic. (2013)
Presentation / Conference Contribution
Sim, K., Hart, E., & Paechter, B. (2013, September). Learning to solve bin packing problems with an immune inspired hyper-heuristic

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 space, we describe an artificial system that discovers and maintains a reperto... Read More about Learning to solve bin packing problems with an immune inspired hyper-heuristic..

Generating single and multiple cooperative heuristics for the one dimensional bin packing problem using a single node genetic programming island model. (2013)
Presentation / Conference Contribution
Sim, K., & Hart, E. (2013, July). Generating single and multiple cooperative heuristics for the one dimensional bin packing problem using a single node genetic programming island model. Presented at 15th annual conference on Genetic and evolutionary computation

Novel deterministic heuristics are generated using Single Node Genetic Programming for application to the One Dimensional Bin Packing Problem. First a single deterministic heuristic was evolved that minimised the total number of bins used when applie... Read More about Generating single and multiple cooperative heuristics for the one dimensional bin packing problem using a single node genetic programming island model..

A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution. (2012)
Presentation / Conference Contribution
Sim, K., Hart, E., & Paechter, B. (2012, September). A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution. Presented at International Conference on Parallel Problem Solving from Nature

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 instance. The EA evolves divisions of variable quantity and dimension that r... Read More about A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution..