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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: Âéw¶¹´«Ã½, 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), Âéw¶¹´«Ã½. 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: DynPORTAL: A highly-configurable benchmark generator for evolutionary dynamic optimization dc.contributor.author: Peng, Mai; Li, Changhe; Yazdani, Danial; Shan, Mengli; Yazdani, Delaram; Li, Miqing; Yang, Shengxiang dc.description.abstract: Dynamic optimization problems (DOPs) require algorithms to track moving optima under changing environments. In evolutionary dynamic optimization (EDO), benchmark generators are essential for standardized evaluation, yet existing generators provide limited landscape complexity and only coarse control beyond basic dynamics. We present DynPORTAL, a dynamic extension of PORTAL that supports ten controllable change properties, including six morphological dynamics for smooth changes and four structural dynamics for abrupt changes, with optional heterogeneous per-component assignments. Integrated into the open-source EDOLAB platform, DynPORTAL supports automated large-scale benchmarking and performance analysis for adaptive algorithms. dc.description: open access article

  • dc.title: Dynamic multi-objective optimization of integrated energy systems in steel enterprises via conditional variational autoencoder dc.contributor.author: Chen, Min; Yang, Shengxiang; Zhang, Yanyan; Zhao, Shengnan dc.description.abstract: To overcome the limitations of conventional evolutionary algorithms in dynamic integrated energy systems for steel enterprises, this paper proposes a Conditional Variational Autoencoder (CVAE) guided multi-objective optimization framework. The method minimizes operational costs and external energy reliance by using online-trained CVAE to map dynamic environmental states (e.g., energy loads, price signals) to optimal solution distributions. Upon detecting changes, the model rapidly generates a state-conditioned initial population, providing a warm start for the evolutionary search. Experiments verify that this framework significantly accelerates convergence and improves solution stability and economic performance in dynamic environments.

  • dc.title: Dynamic electric vehicle routing problem using population-based ant colony optimization dc.contributor.author: Mavrovouniotis, Michalis; Li, Changhe; Chrysostomou, Charalambos; Anastasiadou, Maria; Yang, Shengxiang dc.description.abstract: The population-based ant colony optimization (P-ACO) algorithm has been proven effective in addressing the dy namic electric vehicle routing problem (DEVRP). In the DEVRP the travel time is subject to dynamic changes representing real world traffic conditions. P-ACO maintains an archive of solutions that are used to update the pheromone trails. By default, the archive is updated in first in, first out fashion. In this study, we introduce a new update strategy based on the similarities of the solutions stored in the archive to maintain the diversity when updating the pheromone trails. The experimental results on different DEVRP test cases demonstrate the effectiveness of the proposed strategy in comparison with other existing ones.

  • dc.title: Topological prior-driven sub-region division and vector migration for spatio-temporal region prediction in dynamic multi-objective optimization dc.contributor.author: Hu, Yaru; Zheng, Yunfei; Wang, Sitong; Ou, Junwei; Zheng, Jinhua; Zou, Juan; Yang, Shengxiang dc.description.abstract: Dynamic multi-objective optimization problems (DMOPs) have attracted significant attention in recent years. However, some existing approaches do not fully exploit the geometric structure of the Pareto optimal front, or rely on relatively coarse distance-based matching, which may lead to less accurate solution alignment and degraded prediction performance under dynamic changes. To address these issues, this paper proposes a Topological Prior and Vector Migration-based Multi-Objective Evolutionary Algorithm (TPV-MOEA) to enhance sub-region partitioning and improve prediction accuracy. Specifically, TPV-MOEA incorporates a two-layer graph-based topological aggregation module to aggregate neighborhood information of shared points, enabling topology-aware ordering along the Pareto front manifold for more reliable sub-region partitioning. Within each sub-region, a vector migration strategy is employed to adaptively transfer global MOEA/D reference vectors into local regions, enabling better alignment with the underlying Pareto front structure and achieving more accurate association between non-dominated solutions and their historical counterparts. New populations are generated through direction-guided alignment and position prediction, thereby enabling effective adaptation to dynamic environmental changes. Comparisons with five state-of-the-art algorithms show that TPV-MOEA achieves superior convergence and diversity in tracking the Pareto front under dynamic environments. 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: End-to-end graph-embedded reinforcement learning for solving the shortest path problem with constraints dc.contributor.author: Yang, Shuhao; Huang, Min; Yang, Shengxiang; Zhang, Yuxin; Ma.Lianbo; Wang, Xingwei dc.description.abstract: The shortest path problem (SPP) with constraints constitutes a fundamental yet computationally prohibitive NP-hard challenge in operations research and logistics. Traditional optimization algorithms, including both exact and approximate methods, often suffer from prohibitive computational times and severe scalability bottlenecks on large-scale instances. In contrast, emerging Neural Combinatorial Optimization (NCO) approaches offer the potential for rapid inference but frequently fail to guarantee structural feasibility under strict constraints. To bridge this gap, this study introduces E2E_GERL, a novel end-to-end graph-embedded reinforcement learning algorithm for the time-constrained SPP. The problem is reformulated as a structure-aware and resource-aware sequential decision-making process, where a neural graph embedding network, structure2vec, is integrated to capture the long-term structural equivalence of critical graph nodes. In our framework, a ReLU-based Lagrangian penalty is introduced to embed time constraint violation into the learning objective, and n-step Q-learning is employed to effectively overcome delayed path-level consequences. Extensive experiments on synthetic graphs, modified benchmark instances, and a real-world logistics network demonstrate the superiority of the proposed algorithm, E2E_GERL. It achieves better results with substantially lower inference time than classical and NCO baselines, which also validate the potential of integrating NCO into constrained optimization problem algorithms. dc.description: open access article

  • dc.title: Imbalance-robust retired battery sorting via semi-supervised contrastive representation learning dc.contributor.author: Lei, Cai; Zhao, Wen; Jin, Haiyan; Wang, Bin; Peng, Jichang; Zhang, Ming; Meng, Jinhao; Yang, Shengxiang dc.description.abstract: Effective sorting of retired lithium-ion batteries is a prerequisite for safe and reliable second-life deployment, yet it remains challenging in practice due to heterogeneous degradation behaviours, limited labels, and pronounced class imbalance. To achieve imbalance-robust sorting, this paper proposes a semi-supervised contrastive representation learning framework built upon a unified Visual Geometry Group 16-layer network (VGG16). Specifically, state of charge–discharge voltage curves are transformed into pseudo-colour images and processed by a pre-trained VGG16 backbone for efficient end-to-end feature extraction. To reduce redundancy and highlight discriminative information, convolutional feature responses are further exploited to identify discharge segments exhibiting the most prominent inter-class differences, which are then used as the key inputs for subsequent learning. On this basis, a semi-supervised contrastive objective is introduced to improve intra-class compactness and enlarge inter-class separability, with particular emphasis on strengthening minority-class representations under imbalanced data. Experiments on the public dataset and our private dataset validate the effectiveness of the proposed method, achieving 94.34% accuracy and 0.942 F1-score on the public dataset, and 95.35% accuracy and 0.924 F1-score on the private dataset, respectively. These results suggest that the proposed framework can support fast and reliable retired-battery sorting through a rapid test procedure, thereby reducing sorting uncertainty and safety risks, improving pack consistency for second-life assembly, and facilitating scalable and cost-effective battery reuse. 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: Binary shape-based benchmarks for analyzing search preferences in multi-objective combinatorial optimization dc.contributor.author: Ren, Xuepeng; Yang, Shengxiang; Dai, Guangming; Wang, Maocai dc.description.abstract: Existing multi-objective benchmarks for combinatorial optimization are mainly designed to evaluate convergence and diversity in the objective space, but they offer limited insight into how algorithms explore discrete search spaces under binary representations. In particular, when different algorithms achieve similar Pareto front approximations, it remains unclear whether they exploit the same regions of the decision space or rely on fundamentally different binary structures. To address this limitation, we propose a family of binary shape-based benchmark problems that explicitly decouple three key aspects: (1) the geometry of the Pareto-optimal region, (2) the mapping from binary decision vectors to a low-dimensional geometric space, and (3) analysis methods for revealing search preferences. The proposed benchmarks construct multiple distance-based objectives with respect to simple geometric shapes, including circles, triangles, rectangles, and regular k-gons. Each shape induces a Pareto-optimal region whose interior, edges, and corners correspond to distinct trade-off patterns. In addition, we introduce a cluster-based genotypic analysis framework. Solutions are grouped in the Hamming space of binary decision vectors, characteristic bit-frequency profiles are extracted for each cluster, and the resulting clusters are visualized in the geometric space. Experimental studies using different types of multi-objective evolutionary and local search algorithms demonstrate that methods with similar objective-space performance can exhibit markedly different preferences for regions of the Pareto-optimal set and for genotypic clusters. dc.description: open access article

  • 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.

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)

Âéw¶¹´«Ã½ 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 Âéw¶¹´«Ã½, started in October 2017.

  • William Lawrence: PhD candidate, co-supervised with Dr. Mario Gongora at Âéw¶¹´«Ã½, 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 Âéw¶¹´«Ã½ 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

  • Âéw¶¹´«Ã½ 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.

  • Âéw¶¹´«Ã½ 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.

  • Âéw¶¹´«Ã½ 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

  • Âéw¶¹´«Ã½ 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

  • Âéw¶¹´«Ã½ 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

  • Âéw¶¹´«Ã½ 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

  • Âéw¶¹´«Ã½ 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