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8 results

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...

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...

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...

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...

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 ...

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...

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...

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...