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Professor Shengxiang Yang

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

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

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

Address: 制服无码, The Gateway, Leicester, LE1 9BH UK

T: +44 (0)116 207 8805

E: syang@dmu.ac.uk

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Personal profile

Shengxiang Yang is Professor of Computational Intelligence and Director of the Centre of Computational Intelligence (CCI), 制服无码. 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


  • dc.title: A knee-guided prediction model oriented to population composition structures for dynamic multi-objective evolutionary optimization dc.contributor.author: Huang, Ziwen; Xu, Yue; Pi, Dechang; Yang, Shengxiang dc.description.abstract: There are many multi-objective optimization problems in dynamic environments (DMOPs), characterized by conflicting objectives and changing objective functions over time. Additionally, the dynamic nature of DMOPs may lead to continuous changes in the pareto front. However, existing methods experience significant issues such as severe loss of diversity and slow convergence, which make it challenging to track the dynamic pareto front both accurately and efficiently. To tackle these issues, a knee point guided prediction model is proposed in this article, oriented to population composition structure, which has three original components: (1) Based on the movement trend of previous knee points, the knee point generation strategy combines neighborhood search and step size exploration to identify them in response to environmental changes; (2) Depended on knee point classification, historical non-dominated solutions are reused to cultivate high-quality individuals in new environments, thereby expediting population convergence; (3) Diversity individuals are generated through uniform interpolation between predicted knee points, which increases the distribution of the population. These three strategies are integrated to establish a comprehensive prediction model to direct the generation of initial populations in changing environments, enhancing both the diversity and convergence of population. The effectiveness analysis and performance comparisons with some state-of-the-art algorithms demonstrate that the proposed algorithm exhibits significant advantages in enhancing solution quality. Furthermore, experimental results based on real-world applications validate the practical significance of this study. dc.description: 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.

  • dc.title: A niching archive-assisted evolutionary algorithm for multimodal feature selection dc.contributor.author: Wang, Yunhe; Du, Zhengyu; Zhou, Zeming; Wang, Xubin; Yang, Shengxiang dc.description.abstract: The rapid advancement of data collection technologies has resulted in high-dimensional datasets, which pose significant challenges for machine learning classification tasks. These datasets often include redundant, irrelevant, or noisy features that degrade classification accuracy and increase computational costs. In this context, multimodal feature selection becomes crucial, as it enables the identification and extraction of the most relevant features from the high-dimensional data, thereby enhancing the performance of machine learning models. To tackle this problem, we introduce a Niching Archive-Assisted Particle Swarm Optimization (NAPSO) algorithm designed to address multimodal feature selection. Specifically, NAPSO first introduces a unique K-means-based niche-partitioning method that leverages both feature weights and subset sizes for enhanced diversity preservation and global exploration. It then applies a niche-aware dynamic particle-update rule that adaptively adjusts particles according to fitness, fully exploiting high-quality solutions within each niche. Crucially, a novel probability-guided external elite archive continually retains superior feature subsets and dynamically guides the reinitialization of particles, significantly cutting feature redundancy, boosting classification accuracy, and averting premature convergence. This probabilistic guidance mechanism is a key distinction from existing archive-guided methods. Extensive experiments on diverse high-dimensional datasets reveal that NAPSO demonstrates highly competitive performance, often achieving higher classification accuracy with smaller feature sets on a wide range of datasets, particularly for binary classification problems. Moreover, it discovers several equivalently predictive feature subsets, granting practitioners valuable flexibility for real-world knowledge discovery and data-driven decision-making. dc.description: 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.

  • dc.title: Online spatial-temporal prediction for dynamic constrained multiobjective evolutionary optimization dc.contributor.author: Chen, Guoyu; Guo, Yinan; Yang, Xiao; Zhang, Shunyao; Ma, Tianbing; Yang, Shengxiang; Yuan, Liang dc.description.abstract: The presence of dynamics in dynamic constrained multiobjective optimization problems (DCMOPs) causes the various changes of Pareto optima. The existing methods extract partial historical knowledge to initialize the population at a new time, but neglect inherently temporal and spatial characteristics of dynamic Pareto optima, showing insufficient tracking performance. To solve this issue, a spatial-temporal prediction strategy based dynamic constrained multiobjective evolutionary algorithm is designed in this article, called STPS. Once an environmental change appears, the knowledge construction strategy converts the Pareto optima into the two-dimensional image. All historical images constitute a spatial-temporal series to train the prediction model based on convolutional neural network (CNN) and gated recurrent unit (GRU), initializing a population under the new environment. In addition, an incremental learning strategy is designed to periodically fine-tune the predictor, guaranteeing the prediction accuracy in adapting to the time-varying environments. The intensive experiments on 10 mainstream benchmarks and a real-world case verify that, compared with several state-of-the-art dynamic constrained multiobjective evolutionary algorithms, the proposed algorithm achieves prominent performance in solving DCMOPs. dc.description: 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.

  • dc.title: FDDEDO: A novel federated data-driven evolutionary dynamic optimization framework dc.contributor.author: Wen, Liqun; Wang, Hongfeng; Yang, Shengxiang; Zhang, Yi dc.description.abstract: In the real world, many optimization problems involve time-varying and computationally expensive objective functions. Data-driven surrogate-assisted evolutionary algorithms (SAEAs) are considered promising approaches for solving these problems. However, data-driven SAEAs may face problems in terms of privacy protection and data security when real data are stored in a distributed form across different client devices. Furthermore, the heterogeneity of data stored across different clients further complicates optimization efforts. To address the aforementioned problems, this article proposes a novel federated data-driven evolutionary dynamic optimization framework called FDDEDO. Specifically, to enhance server-side aggregation capabilities, we propose a cosine distance-based surrogate aggregation method that improves the performance of global radial basis function network (RBFN) surrogate through robust RBFN center matching. To cope with environmental changes under limited evaluation budget, a gradient-based client-side surrogate meta-training algorithm is proposed to generate efficient initial local surrogates embedded with prior knowledge for new environments by dynamically learning transfer patterns among historical environments. Meanwhile, to strengthen privacy protection while adapting to heterogeneous data, a client-side surrogate adaptation process based on differential privacy (DP) mechanism is designed. By introducing (系, 未)-DP and combining it with the proximal term used to balance local personalization with global consistency, it achieves effective privacy-preserving fine-tuning for local surrogate under limited samples. Experimental results on benchmark problems under homogeneous and heterogeneous federated settings demonstrate that FDDEDO exhibits significant superiority in overall optimization performance, with all its core components operating effectively. dc.description: 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.

  • dc.title: Neural network-aided differential evolution with double Q-learning for dynamic optimization dc.contributor.author: Song, Wei; Liu, Zhi; Jin, Yaochu; Guo, Yinan; Yang, Shengxiang dc.description.abstract: As the search space and hence the optimum vary through time, dynamic optimization problems (DOPs) bring tremendous difficulties. Changes in DOPs often manifest as diverse dynamics. Consequently, regulating individuals鈥� search to adapt to diverse dynamics is crucial to tackle DOPs. Besides, due to the inherent population nature in dynamic optimization algorithms (DOAs), loss of global and local diversities is a critical issue deteriorating the performance of DOAs. Faced with these difficulties, this article proposes a neural network-aided differential evolution with double Q-learning (NNDE-DQ), in which an evolutionary regulation network (ERN) is designed to maintain high global and local diversities over time and regulate individuals鈥� search that can adapt to diverse dynamics. NNDE-DQ first partitions the search space into multiple subspaces and in each subspace distant individuals are selected as the centers of ERN鈥檚 hidden nodes activated by radial basis function. Every input individual is mutated with two randomly selected hidden node centers from different subspaces as differential individuals, facilitating the maintenance of a high global diversity due to very distinct differential terms of the population. Moreover, each mutated individual selects a hidden node center from the subspace located by the mutated individual to undergo crossover. Due to distant hidden node centers in each subspace, a high local diversity can be maintained by individuals鈥� crossover. Besides, DQ is introduced to acquire ERN鈥檚 desired output by interactively estimating individuals鈥� state-action information, enabling ERN to learn the regulation of individuals鈥� search in dynamic environments and hence adapt to diverse dynamics. The experimental results demonstrate that NNDE-DQ significantly improves the performance in solving various DOPs comparing to seven state-of-the-art DOAs. dc.description: 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.

  • dc.title: Algorithm 1060: EDOLAB, a platform for education and experimentation in evolutionary dynamic optimization dc.contributor.author: Peng, Mai; Yazdani, Delaram; She, Zeneng; Yazdani, Danial; Luo, Wenjian; Li, Changhe; Branke, Juergen; Nguyen, Trung Thanh; Gandomi, Amir H.; Yang, Shengxiang; Jin, Yaochu; Yao, Xin dc.description.abstract: Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization methods. Evolutionary Dynamic Optimization Algorithms (EDOAs) have been developed to address these challenges by adapting to changing environments over time. However, the reproducibility and consistency of experimental results in the literature remain limited due to the lack of publicly available source codes and the complexity of accurately re-implementing algorithms and performance evaluation protocols. To support the community, we introduce Evolutionary Dynamic Optimization LABoratory (EDOLAB), an open source MATLAB platform designed for both research and educational purposes. EDOLAB includes 27 EDOAs, four highly configurable benchmark generators, and a growing suite of performance indicators. The platform supports full parameter tuning, batch experiment management, parallel execution, and automated statistical comparisons鈥攊ncluding rankings, significance testing, box plots, and performance trend visualizations over time. An educational application allows users to observe: (a) dynamic changes in a 2D problem landscape, (b) the movement of individuals in response to these changes, and (c) the ability of an algorithm to track moving optima. By providing an integrated environment for experimentation, benchmarking, and instructional use, EDOLAB promotes reproducibility, comparative analysis, and a deeper understanding of EDOAs in dynamic environments. dc.description: open access article

  • dc.title: Dynamic multi-objective optimization for integrated coal mine energy systems with streaming constraints dc.contributor.author: Zhang, Chi; Rong, Miao; Yang, Shengxiang; Pan, Quanke; Peng, Chen dc.description.abstract: Integrated coal mine energy systems (ICMES) generate streaming constraints where the number of active constraints fluctuates over time due to equipment switching, safety driven operations, and maintenance events. These dynamics cause abrupt contraction, expansion, or fragmentation of the feasible region. To address this challenge, we propose a federated learning (FL) variational autoencoder (VAE) evolutionary algorithm (FVE). Each constraint is mapped to an FL client so that local VAEs can learn heterogeneous constraint specific feasible subspaces as clients observe differently structured constraint data. Federated aggregation fuses these local latent models into a global generative model that adapts quickly and robustly to constraint inflow and outflow. An adaptive population correction mechanism repairs infeasible individuals, and enhanced dynamic dynamic nondominated sorting genetic algorithm-II tracks pareto front evolution under structural shifts. Comparative experiments on benchmark functions and an ICMES scheduling case demonstrate that FVE achieves faster feasibility restoration, improved convergence, and higher diversity than state-of-the-art methods. These results confirm the practicality of FVE for real-time industrial optimization under streaming constraints. dc.description: 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.

  • dc.title: Ensemble of similar differential evolution algorithms with algorithm replacement at position without individual replacement dc.contributor.author: Chen, Chuchuan; Li, Chengjun; Gao, Junbin; Bai, Mingyuan; Yang, Shengxiang dc.description.abstract: Differential evolution exhibits strong performance in real parameter single objective optimization. Ensemble of similar differential evolution algorithms has been proposed in literature. Building on previous research, we introduce a novel ensemble framework of similar differential evolution algorithms, where the primary algorithm initially manages all individuals. If, over a series of generations, an individual consistently fails to be eliminated, one of the secondary algorithms is chosen based on historical performance to provisionally handle the individual鈥檚 position. This ensemble system requires optimization of a single parameter, namely, the upper limit of successive generations without improvement in a position. Based on differential evolution algorithms within the same family, we present two versions of our ensemble. Our experimental analysis utilizes two benchmark test suites and a group of real-world problems. The results reveal that the first version outperforms existing state-of-the-art methods. Furthermore, our ablation experiment on the first version confirms the effectiveness of the proposed ensemble schemes. By observing the performance of the both versions, we establish a relationship between the individual algorithms鈥� performance and that of the ensemble as a whole. dc.description: 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.

  • dc.title: ADR-DMOEA: A dynamic multi-objective optimization evolutionary algorithm based on an adaptive dynamic response strategy dc.contributor.author: Wang, Yuying; Zhou, Ping; Yang, Shengxiang; Chai, Tianyou dc.description.abstract: Optimization problems in real-world applications often involve dynamic environmental changes, requiring algorithms to adapt quickly, track optimal solutions, and maintain efficiency. Existing dynamic multiobjective optimization evolutionary algorithms (DMOEAs) typically rely on fixed or limited dynamic response mechanisms, which are often insufficient to handle complex and varied dynamic environments. To overcome these limitations, this article proposes an adaptive dynamic response-based DMOEA (ADR-DMOEA), which employs a subpopulation-level adaptive mechanism to coordinate diversity-driven, prediction-driven, and memory-driven strategies. The strategy weights are dynamically adjusted according to the static optimization distance of each subpopulation, ensuring that appropriate strategies are adaptively deployed in different environments. This design overcomes the inefficiency of fixed assignments and the instability of individual-level perturbations, enabling coordinated and stable evolution. Extensive experiments on DF benchmark functions and a blast furnace (BF) ironmaking case study demonstrate that ADR-DMOEA achieves superior convergence, diversity, and robustness compared to state-of-the-art algorithms, effectively supporting real-world decision-making under dynamic conditions. dc.description: 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.

  • dc.title: A multi-strategy optimizer for energy minimization of multi-UAV-assisted MEC systems dc.contributor.author: Chen, Yang; Wang, Bi; Yin, Huiping; Yang, Shengxiang dc.description.abstract: Internet of Things (IoT) task offloading involves conflicting objectives of energy consumption and delay. We formulate a bi-objective optimization model for a multi-UAV-assisted mobile edge computing (MEC) system, jointly optimizing resource allocation, task offloading decisions, and UAV deployment to minimize both energy consumption and delay. As the number of offloaded tasks increases, finding feasible solutions becomes more challenging. To address this, we develop an Information Feedback Evolutionary Algorithm (IFEA) that leverages feedback driven guidance to enhance the diversity and convergence of the Pareto front (PF). Simulation results show that IFEA achieves better trade offs than the other four multi-objective algorithms. dc.description: 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.

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)

制服无码 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 (), Intelligent Systems Applications Technical Committee (ISATC), IEEE Computational Intelligence Society (), 2012–2018.

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

  • Senior Member, , since 2014.

  • Member, , 2000 – 2013.

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

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

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

  • Member, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (), Evolutionary Computation Technical Committee (ECTC), IEEE Computational Intelligence Society (), 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 制服无码, started in October 2017.

  • William Lawrence: PhD candidate, co-supervised with Dr. Mario Gongora at 制服无码, 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

  • , 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 制服无码 and , 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

  • 制服无码 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.

  • 制服无码 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.

  • 制服无码 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

  • 制服无码 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

  • 制服无码 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

  • 制服无码 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

  • 制服无码 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

  • Associate Editor (January 2015-now), , Elsevier, UK

  • Associate Editor (January 2015-now), , Taylor and Francis Group, UK

  • Associate Editor (October 2014-now), , IEEE Press, USA

  • Associate Editor (2016-2017), , Elsevier, UK

  • Member of Editorial Board (August 2014-now), , Springer, Germany

  • Member of editorial board (2012-now), , MIT Press, USA

  • Member of editorial board (2007-present), International Journal of Computational Science, Global Information Publisher (GIP), Hong Kong

  • Area editor (2006-present), , World Academic Press, World Academic Union, UK

  • Associate editor (2006-August 2008), Journal of Artificial Evolution and Applications, Hindawi Publishing Corporation, USA

  • Member of editorial board (2009-2010), , IN-TECH Education and Publishing, Austria

  • Guest-editor, Thematic Issue on Memetic Computing in the Presence of Uncertainties, , Vol. 2, No. 2, June 2010, Springer

  • Guest-editor, Special Issue on Evolutionary Computation in Dynamic and Uncertain Environments, , Vol. 7, No. 4, December 2006, Springer

Shengxiang-Yang