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Professor Shengxiang Yang

Job: Professor of Computational Intelligence, Director of the Centre for Computational Intelligence (CCI)

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH UK

T: +44 (0)116 207 8805

E: syang@dmu.ac.uk

W: http://www.tech.dmu.ac.uk/~syang/

 

Personal profile

Shengxiang Yang is Professor of Computational Intelligence and Director of the Centre of Computational Intelligence (CCI), De Montfort University. Before joining the CCI in July 2012, he worked at Brunel University, University of Leicester, and King's College London as a Senior Lecturer, Lecturer, and Post-doctoral Research Associate, respectively.

Shengxiang's main research interests lie in evolutionary computation. He is particularly active in the area of evolutionary computation in dynamic and uncertain environments. Shengxiang has also published on the application of evolutionary computation in communication networks, logistics, transportation systems, and manufacturing systems, etc.

Research group affiliations

Centre for Computational Intelligence

Publications and outputs 

  • A novel scalable framework for constructing dynamic multi-objective optimization problems
    A novel scalable framework for constructing dynamic multi-objective optimization problems Tan, Qingshan; Li, Changhe; Xia, Hai; Zeng, Sanyou; Yang, Shengxiang Modeling dynamic multi-objective optimization problems (DMOPs) has been one of the most challenging tasks in the field of dynamic evolutionary optimization. Based on the analysis of the existing DMOPs, several features widely existed in real-world applications are not taken into account: different objectives may have different function models and variables to be optimized; and the number of conflicting variables should be independent from the number of objectives; the time-linkage property is not considered. In order to overcome the above issues, a novel framework for constructing DMOPs is proposed, where all objectives can be designed independently, and the number of the conflicting variables can be tuned by users. Moreover, it is easy to add new dynamic features to this framework. Several classical dynamic multi-objective optimization algorithms are tested on four scenarios, results show that these characteristics are challenging for the existing algorithms. The file attached to this record is the author's final peer reviewed version.
  • A reinforcement-learning-based evolutionary algorithm using solution space clustering for multimodal optimization problems
    A reinforcement-learning-based evolutionary algorithm using solution space clustering for multimodal optimization problems Xia, Hai; Li, Changhe; Zeng, Sanyou; Tan, Qingshan; Wang, Junchen; Yang, Shengxiang In evolutionary algorithms, how to effectively select interactive solutions for generating offspring is a challenging problem. Though many operators are proposed, most of them select interactive solutions (parents) randomly, having no specificity for the features of landscapes in various problems. To address this issue, this paper proposes a reinforcement-learning-based evolutionary algorithm to select solutions within the approximated basin of attraction. In the algorithm, the solution space is partitioned by the k-dimensional tree, and features of subspaces are approximated with respect to two aspects: objective values and uncertainties. Accordingly, two reinforcement learning (RL) systems are constructed to determine where to search: the objective-based RL exploits basins of attraction (clustered subspaces) and the uncertainty-based RL explores subspaces that have been searched comparatively less. Experiments are conducted on widely used benchmark functions, demonstrating that the algorithm outperforms three other popular multimodal optimization algorithms. The file attached to this record is the author's final peer reviewed version.
  • A random benchmark suite and a new reaction strategy in dynamic multiobjective optimization
    A random benchmark suite and a new reaction strategy in dynamic multiobjective optimization Ruan, Gan; Zheng, Jinhua; Juan, Zou; Ma, Zhongwei; Yang, Shengxiang In the domain of evolutionary computation, more and more attention has been paid to dynamic multiobjective optimization. Generally, artificial benchmarks are effective tools for the performance evaluation of dynamic multiobjective evolutionary algorithms (DMOEAs). After reviewing existing benchmarks and highlighting their weaknesses, this paper proposes a new benchmark suite to promote the comprehensive testing of algorithms. This proposed benchmark suite has eight random instances in which the randomness is produced by designed random time sequences. Also, this suite introduces challenging but rarely considered characteristics, including diverse features in fitness landscape (e.g. deception, multimodality, and bias) and complex trade-off geometries (e.g. convexity-concavity mixed geometry and disconnected geometry). Empirical studies have shown that the proposed benchmark poses reasonable challenges to DMOEAs in terms of convergence and diversity. Besides, a center matching strategy (CMS) is suggested to track random changes in these problems, which applies the history individual information in a global scope for population prediction. Compared with other reaction strategies, CMS has been demonstrated to be very competitive in dealing with random problems. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • An adaptive multi-population evolutionary algorithm for contamination source identification in water distribution systems
    An adaptive multi-population evolutionary algorithm for contamination source identification in water distribution systems Li, Changhe; Yang, Rui; Zhou, Li; Zeng, Sanyou; Mavrovouniotis, Michalis; Yang, Ming; Yang, Shengxiang; Wu, Min Real-time monitoring of drinking water in a water distribution system (WDS) can effectively warn and reduce safety risks. One of the challenges is to identify the contamination source through these observed data due to the real-time, non-uniqueness, and large scale characteristics. To address the real-time and non-uniqueness challenges, we propose an adaptive multi-population evolutionary optimization algorithm to determine the real-time characteristics of contamination sources, where each population aims to locate and track a different global optimum. The algorithm adaptively adjusts the number of populations using a feed-back learning mechanism. To effectively locate an optimal solution for a population, a co-evolutionary strategy is used to identify the location and the injection profile separately. Experimental results on three WDS networks show that the proposed algorithm is competitive in comparison with three other state-of-the-art evolutionary algorithms. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A decision variable classification-based cooperative coevolutionary algorithm for dynamic multiobjective optimization
    A decision variable classification-based cooperative coevolutionary algorithm for dynamic multiobjective optimization Xie, Huipeng; Zou, Juan; Yang, Shengxiang; Zheng, Jinhua; Ou, Junwei; Hu, Yaru This paper proposes a new decision variable classification-based cooperative coevolutionary algorithm, which uses the information of decision variable classification to guide the search process, for handling dynamic multiobjective problems. In particular, the decision variables are divided into two groups: convergence variables (CS) and diversity variables (DS), and different strategies are introduced to optimize these groups. Two kinds of subpopulations are used in the proposed algorithm, i.e., the subpopulations that represent DS and the sub-populations that represent CS. In the evolution process, the coevolution of DS and CS is carried out through genetic operators, and subpopulations of CS are gradually merged into DS, which is optimized in the global search space, based on an indicator to avoid becoming trapped in local optimum. Once a change is detected, a prediction method and a diversity introduction approach are adopted for these two kinds of variables to get a promising population with good diversity and convergence in the new environment. The proposed algorithm is tested on 16 benchmark dynamic multiobjective optimization problems, in comparison with state-of-the-art algorithms. Experimental results show that the proposed algorithm is very competitive for dynamic multiobjective optimization. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A modular neural network-based population prediction strategy for evolutionary dynamic multi-objective optimization
    A modular neural network-based population prediction strategy for evolutionary dynamic multi-objective optimization Li, Sanyi; Yang, Shengxiang; Wang, Yanfeng; Yue, Weichao; Qiao, Junfei This paper presents a novel population prediction algorithm based on modular neural network (PA-MNN) for handling dynamic multi-objective optimization. The proposed algorithm consists of three mechanisms. First, we set up a modular neural network (MNN) and train it with historical population information. Some of the initial solutions are generated by the MNN when an environmental change is detected. Second, some solutions are predicted based on forward-looking center points. Finally, some solutions are generated randomly to maintain the diversity. With these mechanisms, when the new environment has been encountered before, initial solutions generated by MNN will have the same distribution characteristics as the final solutions that were obtained in the same environment last time. Because the initialization mechanism based on the MNN does not need the solutions in recent time, the proposed algorithm can also solve dynamic multi-objective optimization problems with a dramatically and irregularly changing Pareto set. The proposed algorithm is tested on a variety of test instances with different dynamic characteristics and difficulties. The comparisons of experimental results with other state-of-the-art algorithms demonstrate that the proposed algorithm is promising for dealing with dynamic multi-objective optimization. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A feedback-based prediction strategy for dynamic multi-objective evolutionary optimization
    A feedback-based prediction strategy for dynamic multi-objective evolutionary optimization Liang, Zhengping; Zou, Ya; Zheng, Shunxiang; Yang, Shengxiang; Zhu, Zexuan Prediction methods are widely used to solve dynamic multi-objective optimization problems (DMOPs). The key to the success of prediction methods lies in the accurate tracking of the new location of the Pareto set (PS) or Pareto front (PF) in a new environment. To improve the prediction accuracy, this paper proposes a novel feedback-based prediction strategy (FPS), which consists of two feedback mechanisms, namely correction feedback (CF) and effectiveness feedback (EF). CF is used to correct an initial prediction model. When the environment changes, CF constructs a representative individual to reflect the characteristics of the current population. The predicted solution of this individual in the new environment is calculated based on the initial prediction model. Afterward, a step size exploration method based on variable classification is introduced to adaptively correct the prediction model. EF is applied to enhance the effectiveness of re-initialization in two stages. In the first stage, half of the individuals in the population are re-initialized based on the corrected prediction model. In the second stage, EF re-initializes the rest of the individuals in the population using two rounds of roulette method based on the re-initialization effectiveness feedback of the first stage. The proposed FPS is incorporated into a dynamic multi-objective optimization evolutionary algorithm (DMOEA) based on decomposition resulting in a new algorithm denoted as MOEA/D-FPS. MOEA/D-FPS is compared with six state-of-the-art DMOEAs on twenty-two different benchmark problems. The experimental results demonstrate the effectiveness and efficacy of MOEA/D-FPS in solving DMOPs. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Dealing with scarce labelled data: Semi-supervised deep learning with mix match for Covid-19 detection using chest X-ray images
    Dealing with scarce labelled data: Semi-supervised deep learning with mix match for Covid-19 detection using chest X-ray images Calderon-Ramirez, Saul; Giri, Raghvendra; Yang, Shengxiang; Moemeni, Armaghan; Umana, Mario; Elizondo, David; Torrents-Barrena, Jordina; Molina-Cabello, Miguel A. Coronavirus (Covid-19) is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently available tests are scarce and limited to people in danger of serious illness. The application of deep learning to chest X-ray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed. We develop and test a semi-supervised deep learning framework based on the Mix Match architecture to classify chest X-rays into Covid-19, pneumonia and healthy cases. The presented approach was calibrated using two publicly available datasets. The results show an accuracy increase of around 15% under low labelled / unlabelled data ratio. This indicates that our semi-supervised framework can help improve performance levels towards Covid-19 detection when the amount of high-quality labelled data is scarce. Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison. The file attached to this record is the author's final peer reviewed version.
  • An adaptive localized decision variable analysis approach to large scale multi-objective and many-objective optimization
    An adaptive localized decision variable analysis approach to large scale multi-objective and many-objective optimization Ma, Lianbo; Huang, Min; Yang, Shengxiang; Wang, Rui; Wang, Xingwei This paper proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large scale multi-objective and many objective optimization problems. Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large scale multiobjective and many-objective optimization problems. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Evolutionary multiobjective clustering algorithms with ensemble for patient stratification
    Evolutionary multiobjective clustering algorithms with ensemble for patient stratification Wang, Yunhe; Li, Xiangtao; Wong, Ka-Chun; Chang, Yi; Yang, Shengxiang Patient stratification has been studied widely to tackle subtype diagnosis problems for effective treatment. Due to the dimensionality curse and poor interpretability of data, there is always a long-lasting challenge in constructing a stratification model with high diagnostic ability and good generalization. To address these problems, this paper proposes two novel evolutionary multiobjective clustering algorithms with ensemble (NSGA-II-ECFE and MOEA/D-ECFE) with four cluster validity indices used as the objective functions. First, an effective ensemble construction method is developed to enrich the ensemble diversity. After that, an ensemble clustering fitness evaluation (ECFE) method is proposed to evaluate the ensembles by measuring the consensus clustering under those four objective functions. To generate the consensus clustering, ECFE exploits the hybrid co-association matrix from the ensembles and then dynamically selects the suitable clustering algorithm on that matrix. Multiple experiments have been conducted to demonstrate the effectiveness of the proposed algorithm in comparison with seven clustering algorithms, twelve ensemble clustering approaches, and two multiobjective clustering algorithms on 55 synthetic datasets and 35 real patient stratification datasets. The experimental results demonstrate the competitive edges of the proposed algorithms over those compared methods. Furthermore, the proposed algorithm is applied to extend its advantages by identifying cancer subtypes from five cancer-related single-cell RNA-seq datasets. The file attached to this record is the author's final peer reviewed version.

Click here to view a full listing of Shengxiang Yang's publications and outputs.

Research interests/expertise

  • Evolutionary Computation

  • Swarm Intelligence

  • Meta-heuristics

  • Dynamic Optimisation Problems

  • Multi-objective Optimisation Problems

  • Relevant Real-World Applications

Areas of teaching

Research Methods for Intelligent Systems and Robotics MSc, Software Engineering MSc, Computing MSc, and Business Intelligence Systems and Data Mining MSc Degrees.

Qualifications

BSc in Automatic Control, Northeastern University, China (1993)

MSc in Automatic Control, Northeastern University, China (1996)

PhD in Systems Engineering Northeastern University, China (1999)

Courses taught

I have taught numerous modules at both undergraduate and postgraduate level. Quite a number of modules I taught were significantly developed by myself. The modules I taught are usually designed to be practice-oriented with problem-solving lab sessions based on Java or C++ programming, and hence are highly interesting to and greatly useful for students. They are also very important for different degree programmes in Computer Science and relevant subjects. Some of the modules I have taught are listed as follows:

  • CS3002 Artificial Intelligence (2010 – 2012, Brunel University): 3rd year Computer Science (Artificial Intelligence) BSc module, module leader

  • CS2005 Networks and Operating Systems (2010 – 2012, Brunel University): 2nd year Network Computing BSc module, part module

  • CS5518 Business Integration (2011-2012, Brunel University): Business Systems Integration MSc module, part module

  • CO2017 Networks and Distributed Systems (2005–2010, University of Leicester): 2nd year Computer Science BSc module, module leader

  • CO2005 Object-Oriented Programming Using C++ (2006–2009, University of Leicester): 2nd year Computer Science BSc module, module leader

  • CO1003 Program Design (2006-2007, University of Leicester): 1st year Computer Science BSc module, module leader

  • CO3097 Programming Secure and Distributed Systems (2003–2005, University of Leicester): 3rd year Computer Science BSc & Advanced Computer Science MSc module, module leader

  • CO1017 Operating Systems and Networks (2001 – 2004, University of Leicester): 1st year Computer Science BSc module, module leader

  • CO1016 Computer Systems (2000 – 2002, University of Leicester): 1st year Computer Science BSc module, part module

I have also co-ordinated several BSc projects, as shown below.

  • CS3072/CS3074/CS3105/CS3109 BSc Final Year Projects (2010 – 2012, Brunel University): Co-ordination Team Member

  • CO3012/CO3013/CO3015 Computer Science BSc Final Year Projects (2004 – 2010, University of Leicester): Co-ordinator

  • CO3120 Computer Science with Management BSc Final Year Project (2007 – 2010, University of Leicester): Co-ordinator

  • CO3014 Mathematics and Computer Science BSc Final Year Project (2004 – 2010, University of Leicester): Co-ordinator

  • CO2015 Second Year BSc Software Engineering Project (2003 – 2004, University of Leicester): Co-ordinator

Honours and awards

  • Nominatee to the Best Paper Award for EvoApplications 2016: Applications of Evolutionary Computation, for the paper "Direct memory schemes for population-based incremental learning in cyclically changing environments" by Michalis Mavrovouniotis and Shengxiang Yang, published in EvoApplications 2016: Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol. 9598, pp. 233-247, 2016.

  • Nominatee for the Best-Paper Award of the ACO-SI Track at the 2015 Genetic and Evolutionary Computation Conference, for the paper "An ant colony optimization based memetic algorithm for the dynamic travelling salesman problem" by Michalis Mavrovouniotis, Felipe Martins Muller and Shengxiang Yang, published in the Proceedings of the 17th Annual Conference on Genetic and Evolutionary Computation, pp. 49-56, 2015.

  • Winner of the 2014 IEEE Congress on Evolutionary Computation Best Student Paper Award, for the paper entitled "A test problem for visual investigation of high-dimensional multi-objective search" by Miqing Li, Shengxiang Yang and Xiaohui Liu, published in the Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 2140-2147, 2014.

  • Nominatee for the 2005 Genetic and Evolutionary Computation Conference Best Paper Award, for the paper "Memory-based immigrants for genetic algorithms in dynamic environments" by Shengxiang Yang, published in the Proceedings of the 2005 Genetic and Evolutionary Computation Conference, Vol. 2, pp. 1115-1122, 2005.

  • Visiting Professor (2012 – 2014, 2016-2018), College of Information Engineering, Xiangtan University, China

  • Visiting Professor (2011 – 2017), College of Mathematics and Statistics, Nanjing University of Information Science and Technology, China

Membership of professional associations and societies

  • Founding Chair, Task Force on Intelligent Network Systems (TF-INS), Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (IEEE CIS), 2012–2018.

  • Chair, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), 2011–2018.

  • Senior Member, IEEE, since 2014.

  • Member, IEEE, 2000 – 2013.

  • Member, IEEE Computational Intelligence Society (IEEE CIS), since 2005.

  • Member, Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), since 2011.

  • Member, Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (IEEE CIS), since 2013.

  • Member, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (IEEE CIS), 2003 – 2010.

Current research students

First Supervisor:

  • Muhanad Tahrir Younis: Swarm intelligence for dynamic job scheduling in grid computing, started from October 2014

  • Conor Fahy: Evolutionary computation for data stream analysis, started from October 2015

  • Zedong Zheng: started from October 2016
  • Matthew Fox: started from October 2017

Second Supervisor:

  • Ahad Arshad: PhD candidate, co-supervised with Prof. Paul Fleming at De Montfort University, started in October 2017.
  • William Lawrence: PhD candidate, co-supervised with Dr. Mario Gongora at De Montfort University, started in April 2012

Complete PhD Students (I was the 1st Supervisor):

  • Changhe Li: Particle swarm optimisation in stationary and dynamic environments, 2011

  • Imtiaz Ali Korejo: Adaptive mutation operators for evolutionary algorithms, 2011

  • Sadaf Naseem Jat: Genetic algorithms for university course timetabling problems, 2012

  • Shakeel Arshad: Sequence based memetic algorithms for static and dynamic travelling salesman problems, 2012

  • Michalis Mavrovouniotis: Ant Colony Optimization in Stationary and Dynamic Environments, 2013

  •  Miqing Li: Evolutionary Many-Objective Optimization: Pushing the Boundaries, 2015
  • Jayne Eaton: Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems, 2017
  • Shouyong Jiang: Evolutionary Algorithms for Static and Dynamic Multiobjective Optimization, 2017

Externally funded research grants information

  • EU Horizon 2020 Marie Sklodowska-Curie Individual Fellowships (PI, Project ID: 661327, 09/2015-08/2017, €195,455): Evolutionary Computation for Dynamic Constrained Optimization Problems (ECDCOP)
  • EPSRC (PI, Standard Research Project, EP/K001310/1, 18/2/2013-17/02/2017, £445,069): Evolutionary Computation for Dynamic Optimisation in Network Environments

  • EPSRC (PI, Standard Research Project, EP/E060722/1 and EP/E060722/2, 1/1/2008-1/7/2011, £307,469): Evolutionary Algorithms for Dynamic Optimisation Problems: Design, Analysis and Applications

  • EPSRC (PI, Overseas Travel Grants GR/S79718/01, 1/11/2003-31/1/2004, £6,700): Adaptive and Hybrid Genetic Algorithms for Production Scheduling Problems in Manufacturing. This grant supported my research visit to Waseda University, Japan, during my Sabbatical leave period. Additionally, Waseda University, Japan contributed JPY140,000 (~£800) toward the visit

  • State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, China (PI, Open Research Project, 1/1/2012-31/12/2013, CNY300,000 (~£30,000)): Evolutionary Computation for Dynamic Scheduling Problems in Process Industries

  • State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, China (PI, Open Research Project, 1/1/2010-31/12/2011, CNY150,000 (~£15,000)): Evolutionary Computation for Dynamic Optimization and Scheduling Problems

  • Transport iNet, European Regional Development Fund (Co-I, 11/11/2013 - 28/02/2015, £62,134), Evolutionary Computation for Optimised Rail Travel (EsCORT). This is a linked project between De Montfort University and Go Travel Solutions, a Leicester based SME specialising in assisting businesses to develop sustainable travel solutions, covering people and goods.
  • Hong Kong Polytechnic University Research Grants (Co-I, Grant G-YH60, 1/7/2009-30/6/2010, HKD120,000 (~£10,000)): Improved Evolutionary Algorithms with Primal-Dual Population for Dynamic Variation in Production Systems. Partners:

In addition, I have also received several conference travel grants from UK Research Councils, e.g., Royal Society Conference Travel Grant (£700 in 2007 and £719 in 2005) and Royal Academy of Engineering Conference Grant (£800 in 2007 and £1,200 in 2006).

Internally funded research project information

  • De Montfort University Higher Education Innovation Fund (HEIF) 2017-18 (Co-I, 01/12/2017-31/07/2018, £14,000): Brian-Computer-Interface Prototyping System: Data-based Filtering and Dynamic Characterisation.
  • De Montfort University Higher Education Innovation Fund (HEIF) 2015-16 (PI, 01/01/2016-31/07/2016, £24,800): Development of a Dynamic Resource Scheduling Prototype System for Airports.

  • De Montfort University PhD Studentships 2017-18 (PI, 1/10/2017–30/09/2020, approximately £60,000): supporting stipend and fees for one EU/Home PhD student for three years

  • De Montfort University Fee Waiver PhD Scholarships 2016-17 (PI, 1/10/2016–30/09/2019, approximately £40,000): supporting fees for one overseas PhD student for three years

  • De Montfort University PhD Studentships 2015-16 (PI, 1/10/2015–30/09/2018, approximately £60,000): supporting stipend and fees for one EU/Home PhD student for three years

  • De Montfort University PhD Studentships 2013-14 (PI, 1/10/2013–30/09/2016, approximately £80,000): supporting stipend and fees for one overseas PhD student for three years

  • De Montfort University PhD Studentships 2013-14 (PI, 1/4/2013–31/03/2016, approximately £60,000): supporting stipend and fees for one home PhD student for three years

  • Brunel University PhD Studentships 2011-12 (PI, 01/10/2011–30/09/2014, approximately £80,000): supporting stipend and fees for one overseas PhD student for three years

  • University of Leicester PhD Studentships 2008-09 (PI, 1/10/2008–30/9/2011, approximately £50,000): supporting stipend and fees for one PhD student for three years

  • University of Leicester Research Fund 2001 (PI, 1/1/2001- 31/12/2001, £3,200): Using Neural Network and Genetic Algorithm Methods for Job-Shop Scheduling Problem.

Professional esteem indicators

Shengxiang-Yang