Data Scientist (KTP Associate). PhD, MSc, BSc.

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portfolio

publications

Automated design of search algorithms: Learning on algorithmic components

Published in Expert Systems with Applications, 2021

Highlights

  • A framework for automated design of local search algorithms.
  • Various local search algorithms are modelled within the framework.
  • The vehicle routing problems are used as the domain problem.
  • Performance of elementary algorithmic components is analysed.
  • Two learning models based on reinforcement learning and Markov chain are compared.

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

Sequential Rule Mining for Automated Design of Meta-heuristics

Published in The Genetic and Evolutionary Computation (GECCO), 2023

Abstract

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 algorithm design. This paper investigates effective algorithmic compositions with sequential rule mining techniques to discover valuable knowledge in algorithm design. With the collected effective algorithmic compositions, sequential rules of basic algorithmic components are extracted and further analysed to automatically compose basic algorithmic components within the general AutoGCOP framework to develop new effective meta-heuristics. The sequential rules present superior performance in composing the basic algorithmic components for solving the benchmark vehicle routing problems with time window constraints, demonstrating its effectiveness in designing new algorithms automatically.

Recommended citation: Meng, W., & Qu, R. (2023, July). Sequential Rule Mining for Automated Design of Meta-heuristics. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (pp. 1727-1735). https://doi.org/10.1145/3583133.3596303

Automated design of local search algorithms: Predicting algorithmic components with LSTM

Published in Expert Systems with Applications, 2023

Highlights

  • The design of local search algorithms is defined as a sequence classification task
  • LSTM is applied to forecast algorithmic components for automated composition
  • LSTM has a better classification performance as compared with other classifiers
  • Key features for sequence classification are identified to support algorithm design

Recommended citation: Meng, W., & Qu, R. (2023). Automated design of local search algorithms: Predicting algorithmic components with LSTM. Expert Systems with Applications, [DOI: 10.1016/j.eswa.2023.121431](https://doi.org/10.1016/j.eswa.2023.121431) https://doi.org/10.1016/j.eswa.2023.121431

talks

teaching

BSc/MSc Project Ideas

, Computer Science, University of Nottingham, 2023

MSc Supervision

Selected previous BSc/MSc projects:

  • Machine Learning-assisted Automatic Configuration of Metaheuristics for Vehicle Routing Problems.
  • Research on Search Algorithms for Solving Capacitated Vehicle Routing Problem Based on Reinforcement Learning.
  • A Data Visualisation Tool to Support the Automated Algorithm Design.