Professor Xin Yao BSc, MSc, PhD

Prof Xin Yao

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

Chen, T, Bahsoon, R & Yao, X 2020, 'Synergizing domain expertise with self-awareness in software systems: a patternized architecture guideline', Institute of Electrical and Electronics Engineers. Proceedings . https://doi.org/10.1109/JPROC.2020.2985293

Zhen, L, Peng, D, Wang, W & Yao, X 2020, 'Kernel truncated regression representation for robust subspace clustering', Information Sciences, vol. 524, pp. 59-76. https://doi.org/10.1016/j.ins.2020.03.033

Hierons, RM, Li, M, Liu, X, Parejo, JA, Segura, S & Yao, X 2020, 'Many-objective test suite generation for software product lines', ACM Transactions on Software Engineering and Methodology, vol. 29, no. 1, 2. https://doi.org/10.1145/3361146

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, 5. 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, pp. 1-26. https://doi.org/10.1162/evco_a_00269

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

Conference contribution

Friess, S, Tino, P, Menzel, S, Sendhoff, B & Yao, X 2020, Learning Across Problem Instances: Improving Sampling in Evolution Strategies through Mixture-based Distributions. in Learning Across Problem Instances: Improving Sampling in Evolution Strategies through Mixture-based Distributions.

Ruan, G, Minku, L, Menzel, S, Sendhoff, B & Yao, X 2020, Computational study on effectiveness of knowledge transfer in dynamic multi-objective optimisation. in 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020). IEEE Computer Society Press, 2020 IEEE Congress on Evolutionary Computation (IEE CEC 2020), Glasgow, United Kingdom, 19/07/20.

Saha, S, Rios, T, Minku, L, Yao, X, Xu, Z, Sendhoff, B & Menzel, S 2020, Optimal evolutionary optimization hyper-parameters to mimic human user behavior. in 2019 IEEE Symposium Series on Computational Intelligence (EEE SSCI 2019). IEEE Symposium Series on Computational Intelligence (SSCI), IEEE Computer Society Press, 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6/12/19. https://doi.org/10.1109/SSCI44817.2019.9002958

Minku, L, Saha, S, Rios, T, Yao, X, Xu, Z, Sendhoff, B & Menzel, S 2019, Optimal Evolutionary Optimization Hyperparameters to Mimic Human User Behaviour. in 2019 IEEE Symposium Series on Computational Intelligence (SSCI). pp. 858-866. https://doi.org/10.1109/SSCI44817.2019.9002958

Friess, S, Tino, P, Menzel, S, Sendhoff, B & Yao, X 2019, Learning Transferable Variation Operators in a Continuous Genetic Algorithm. in 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)., 9002976, Institute of Electrical and Electronics Engineers (IEEE), pp. 2027-2033, 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019, Xiamen, China, 6/12/19. https://doi.org/10.1109/SSCI44817.2019.9002976

Minku, L, Ruan, G, Menzel, S, Sendhoff, B & Yao, X 2019, When and How to Transfer Knowledge in Dynamic Multi-Objective Optimisation. in 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Computer Society Press, pp. 2034-2041. <https://ieeexplore.ieee.org/document/9002815>

Saha, S, Rios, T, Menzel, S, Sendhoff, B, Back, T, Yao, X, Xu, Z & Wollstadt, P 2019, Learning time-series data of industrial design optimization using recurrent neural networks. in P Papapetrou, X Cheng & Q He (eds), Proceedings - 19th IEEE International Conference on Data Mining Workshops (ICDMW 2019)., 8955564, IEEE Computer Society, pp. 785-792, 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019, Beijing, China, 8/11/19. https://doi.org/10.1109/ICDMW.2019.00116

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

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