Professor Xin Yao BSc, MSc, PhD

Prof Xin Yao

School of Computer Science
Professor of Computer Science
Director of the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA)

Contact details

Address
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. Xin is also the Director of the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA)

For more information, please visit Xin's School webpage.

Postgraduate supervision

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

Li, M & Yao, X 2019, 'Quality evaluation of solution sets in multiobjective optimisation: a survey', ACM Computing Surveys, vol. 52, no. 2, 26. https://doi.org/10.1145/3300148

Song, L, Minku, LL & Yao, X 2019, 'Software effort interval prediction via Bayesian inference and synthetic Bootstrap resampling', ACM Transactions on Software Engineering and Methodology, vol. 28, no. 1. https://doi.org/10.1145/3295700

Li, M & Yao, X 2018, 'What weights work for you? adapting weights for any pareto front shape in decomposition-based evolutionary multiobjective optimisation', Evolutionary Computation.

Li, K, Chen, R, Min, G & Yao, X 2018, 'Integration of Preferences in Decomposition Multiobjective Optimization', IEEE Transactions on Cybernetics, vol. 48, no. 12, pp. 3359-3370. https://doi.org/10.1109/TCYB.2018.2859363

Li, K, Deb, K & Yao, X 2018, 'R-Metric: Evaluating the Performance of Preference-Based Evolutionary Multi-Objective Optimization Using Reference Points ', IEEE Transactions on Evolutionary Computation, vol. 22, no. 6, pp. 821 - 835. https://doi.org/10.1109/TEVC.2017.2737781

Xue, X & Yao, X 2018, 'Interactive ontology matching based on partial reference alignment', Applied Soft Computing, vol. 72, pp. 355-370. https://doi.org/10.1016/j.asoc.2018.08.003

Wang, S, Minku, LL & Yao, X 2018, 'A Systematic Study of Online Class Imbalance Learning With Concept Drift', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 4802-4821. https://doi.org/10.1109/TNNLS.2017.2771290

Cheng, R, Li, M, Li, K & Yao, X 2018, 'Evolutionary Multiobjective Optimization Based Multimodal Optimization: Fitness Landscape Approximation and Peak Detection', IEEE Transactions on Evolutionary Computation, vol. 22, no. 5, pp. 692 - 706. https://doi.org/10.1109/TEVC.2017.2744328

Li, K, Chen, R, Fu, G & Yao, X 2018, 'Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization', IEEE Transactions on Evolutionary Computation, pp. 1-1. https://doi.org/10.1109/TEVC.2018.2855411

Chen, T, Li, K, Bahsoon, R & Yao, X 2018, 'FEMOSAA: Feature-Guided and Knee-Driven Multi-Objective Optimization for Self-Adaptive Software', ACM Transactions on Software Engineering and Methodology, vol. 27, no. 2, 2. https://doi.org/10.1145/3234930

Chen, T, Bahsoon, R & Yao, X 2018, 'A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systems', ACM Computing Surveys, vol. 51, no. 3, 61. https://doi.org/10.1145/3212709

Ma, H, Li, Z, Tayarani, M, Lu, G, Xu, H & Yao, X 2018, 'Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. https://doi.org/10.1177/0954407018776743

Ma, H, Li, Z, Tayarani, M, Lu, G, Xu, H & Yao, X 2018, 'Computational Intelligence Nonmodel-Based Calibration Approach for Internal Combustion Engines', Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 140, no. 4, 041002. https://doi.org/10.1115/1.4037835

Conference contribution

Li, M & Yao, X 2019, An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods. in K Deb, E Goodman, C Coello Coello, K Klamroth, K Miettinen, S Mostaghim & P Reed (eds), Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. Lecture Notes in Computer Science - Theoretical Computer Science and General Issues, vol. 11411, Springer, pp. 15-26, 10th International Conference on Evolutionary Multi-Criterion Optimization, (EMO 19), East Lansing, Michigan, United States, 10/03/19. https://doi.org/10.1007/978-3-030-12598-1_2

SONG, L, MINKU, LL & YAO, X 2018, A novel automated approach for software effort estimation based on data augmentation. in G T. Leavens, A Garcia & C S. Păsăreanu (eds), Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018). ACM/IEEE, New York, NY, pp. 468-479, The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018), Lake Buena Vista, United States, 4/11/18. https://doi.org/10.1145/3236024.3236052

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