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Outputs (4)

Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition (2024)
Presentation / Conference Contribution
Meng, W., Qu, R., & Pillay, N. (2023, July). Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition. Presented at Proceedings of the Genetic and Evolutionary Computation Conference Companion, Lisbon

The Competition of Machine Learning for Evolutionary Computation for Solving Vehicle Routing Problems (ML4VRP) seeks to bring together machine learning and evolutionary computation communities to propose innovative techniques for vehicle routing prob... Read More about Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition.

Automated design of local search algorithms: Predicting algorithmic components with LSTM (2023)
Journal Article
Meng, W., & Qu, R. (2024). Automated design of local search algorithms: Predicting algorithmic components with LSTM. Expert Systems with Applications, 237(Part A), Article 121431. https://doi.org/10.1016/j.eswa.2023.121431

With a recently defined AutoGCOP framework, the design of local search algorithms has been defined as the composition of elementary algorithmic components. The effective compositions of the best algorithms thus retain useful knowledge of effective al... Read More about Automated design of local search algorithms: Predicting algorithmic components with LSTM.

Sequential Rule Mining for Automated Design of Meta-heuristics (2023)
Presentation / Conference Contribution
Meng, W., & Qu, R. (2023, July). Sequential Rule Mining for Automated Design of Meta-heuristics. Presented at GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion, New York, USA

With a recently defined AutoGCOP framework, the design of local search algorithms can be defined as the composition of the basic elementary algorithmic components. These compositions into the best algorithms thus retain useful knowledge of effective... Read More about Sequential Rule Mining for Automated Design of Meta-heuristics.

Automated design of search algorithms: Learning on algorithmic components (2021)
Journal Article
Meng, W., & Qu, R. (2021). Automated design of search algorithms: Learning on algorithmic components. Expert Systems with Applications, 185, Article 115493. https://doi.org/10.1016/j.eswa.2021.115493

This paper proposes AutoGCOP, a new general framework for automated design of local search algorithms. In a recently established General Combinatorial Optimisation Problem (GCOP) model, the problem of algorithm design itself is defined as a combinato... Read More about Automated design of search algorithms: Learning on algorithmic components.