Professor Xin Yao BSc, MSc, PhD

School of Computer Science
Professor of Computer Science

Contact details

Address
School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Professor Xin Yao is a Professor of Computer Science in the School of Computer Science, at the University of Birmingham. 

Professor Yao also operates ECOLE, an Innovative Training Network (ITN) for early stage researchers (ESRs) funded by the EU’s Horizon 2020 research and innovation program under grant agreement No.766186. It is based on novel synergies between nature inspired optimisation and machine learning. The training programme will be targeted at the automotive industry and ESRs employed on the program will be provided with the transferable skills necessary for thriving careers in emerging and rapidly developing industrial areas.

Please follow the link below to find out more about Professor Yao's work:

Professor Yao's-personal webpage.

Research

Professor Yao’s research interests include:

  • Evolutionary computation (evolutionary optimisation, evolutionary learning, evolutionary design)
  • Neural network ensembles and multiple classifiers (especially on the diversity issue)
  • Meta-heuristic algorithms
  • Data mining
  • Global optimisation
  • Simulated annealing
  • Computational complexity of evolutionary algorithms, and various real-world applications

Publications

Recent publications

Article

Herring, D, Kirley, M & Yao, X 2024, 'A comparative study of evolutionary approaches to the bi-objective dynamic Travelling Thief Problem', Swarm and Evolutionary Computation, vol. 84, 101433. https://doi.org/10.1016/j.swevo.2023.101433

Tong, H, Minku, L, Menzel, S, Sendhoff, B & Yao, X 2024, 'Evaluating Meta-heuristic Algorithms for Dynamic Capacitated Arc Routing Problems Based on a Novel Lower Bound Method', IEEE Computational Intelligence Magazine, vol. 19, no. 4, 10709780, pp. 31-44. https://doi.org/10.1109/MCI.2024.3440213

Ruan, G, Minku, L, Xu, Z & Yao, X 2024, 'Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network', IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/TETCI.2024.3499997

Ruan, G, Minku, L, Menzel, S, Sendhoff, B & Yao, X 2024, 'Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives', IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/TETCI.2024.3389769

Ruan, G, Minku, L, Menzel, S, Sendhoff, B & Yao, X 2024, 'Learning to Expand/Contract Pareto Sets in Dynamic Multi-objective Optimization with a Changing Number of Objectives', IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2024.3375751

Shi, X, Minku, L & Yao, X 2023, 'Evolving Memristive Reservoir', IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2023.3270224

Zhang, S, Tino, P & Yao, X 2023, 'Hierarchical reduced-space drift detection framework for multivariate supervised data streams', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 3, pp. 2628-2640. https://doi.org/10.1109/TKDE.2021.3111756

Zhang, G, Su, Z, Shao, Z, Li, M, Li, B, Yao, X & Li, L 2023, 'New Reliability-Driven Bounds for Architecture-Based Multi-Objective Testing Resource Allocation', IEEE Transactions on Software Engineering, vol. 49, no. 4, pp. 2513-2529. https://doi.org/10.1109/TSE.2022.3223875

Song, L, Minku, L & Yao, X 2023, 'On the Validity of Retrospective Predictive Performance Evaluation Procedures in Just-In-Time Software Defect Prediction', Empirical Software Engineering, vol. 28, no. 5, 124. https://doi.org/10.1007/s10664-023-10341-8

Su, Z, Li, M, Zhang, G, Wu, Q, Li, M, Zhang, W & Yao, X 2023, 'Robust Audio Copy-Move Forgery Detection Using Constant Q Spectral Sketches and GA-SVM', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 5, pp. 4016-4031. https://doi.org/10.1109/TDSC.2022.3215280

Conference contribution

Shi, X, Minku, L & Yao, X 2024, Novel Memristive Reservoir Computing with Evolvable Topology for Time Series Prediction. in 31st International Conference on Neural Information Processing (ICONIP'2024). Springer, 31st International Conference on Neural Information Processing, Auckland, New Zealand, 2/12/24.

Shi, X, Minku, L & Yao, X 2024, Tree-based Genetic Programming for Evolutionary Analog Circuit with Approximate Shapley Value. in M Bramer & F Stahl (eds), Artificial Intelligence XLI: 44th SGAI International Conference on Artificial Intelligence, AI 2024, Cambridge, UK, December 17–19, 2024, Proceedings, Part I. Lecture Notes in Artificial Intelligence, vol. 15446, Springer, 44th SGAI International Conference on Artificial Intelligence, Cambridge, United Kingdom, 17/12/24. https://doi.org/10.1007/978-3-031-77915-2_18

Tong, H, Minku, L, Menzel, S, Sendhoff, B & Yao, X 2023, A Novel Generalized Metaheuristic Framework for Dynamic Capacitated Arc Routing Problems. in GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 45–46, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583133.3595829

Song, L, Minku, L, Teng, C & Yao, X 2023, A Practical Human Labeling Method for Online Just-in-Time Software Defect Prediction. in S Chandra, K Blincoe & P Tonella (eds), ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. FSE: Foundations of Software Engineering, Association for Computing Machinery (ACM), pp. 605–617, 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, San Francisco, California, United States, 3/12/23. https://doi.org/10.1145/3611643.3616307

Shi, X, Wang, Z, Minku, L & Yao, X 2023, Explaining Memristive Reservoir Computing Through Evolving Feature Attribution. in GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 683–686. https://doi.org/10.1145/3583133.3590619

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