Research explorer tool

23 results

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 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 ...

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 Neural Approach to Generation of Constructive Heuristics

Conference Proceeding
Alissa, M., Sim, K., & Hart, E. (2021)
A Neural Approach to Generation of Constructive Heuristics. In 2021 IEEE Congress on Evolutionary Computation (CEC) (1147-1154). https://doi.org/10.1109/CEC45853.2021.9504989
Both algorithm-selection methods and hyper-heuristic methods rely on a pool of complementary heuristics. Improving the pool with new heuristics can improve performance, howeve...

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...

Minimising line segments in linear diagrams is NP-hard

Journal Article
Chapman, P., Sim, K., & Hao Chen, H. (2022)
Minimising line segments in linear diagrams is NP-hard. Journal of Computer Languages, 71, Article 101136. https://doi.org/10.1016/j.cola.2022.101136
Linear diagrams have been shown to be an effective method of representing set-based data. Moreover, a number of guidelines have been proven to improve the efficacy of linear d...

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
In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packi...

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...

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 Sciences. https://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...

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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