Click here to open a Java window and run the GA_Demo...


Evolutionary Computer-Automated Design (CAutoD) and Virtual Prototyping for Industry 4.0

To run GA / EA Demo in a Java window, click here or on the CAutoD animation 

This is an interactive courseware to show users step by step how a genetic algorithm works.  You can also watch global convergence in a batch mode, change the population size, crossover rates, mutation rates, selection mechanisms, and/or add a constraint. 

Since 1991, the author (Yun Li) has been teaching at University of Glasgow and wrote this applet for his "Neural and Evolutionary Computing" course. His Intelligent Systems group research into transforming the passive Computer-Aided Design (CAD) to the pro-active Computer-Automated Design (CAutoD) and machine invention, especially for "Industry 4.0" With data-driven prospects of computation akin to the human being, CAutoD can offer automation functions as services for seamless cyber-physical integration globally, and is applicable to electronic, electrical, mechanical, control, and biomedical engineering, operations management, financial and economic system modelling and optimisation.

Invitation to Energies (Impact Factor 2.1) Special Issue on "Smart Creativity for Manufacturing and Industry 4.0", submission by 31 December 2016, scheduled for March 2017 publication.

The "Fourth Industrial Revolution" is dawning upon us... This Special Issue is devoted to Industry 4.0, the first a-priori engineered (and the fourth) 'Industrial Revolution'Initiated by the supply side, this will be the first time that the customer has a prospect of 'demanding' what they want at a mass production price - the entire manufacturing value chain is now set to be transformed, globally rapidly...

WCCI'16: Computational Intelligence for Industry 4.0 Special Session

Focusing on smart manufacturing and cyber-physical systems so far, efforts in Industry 4.0 have lacked smart design and business elements for manufacture that are necessary in completing this unprecedented upgrade of manufacturing value chain.  Computational intelligence has however provided an extra-numeric, as well as efficiently-numeric, tool to realise this goal. 

WCCI'16: Key Challenges and Future Directions of Evolutionary Computation

Panel Members:

Yun Li, University of Glasgow, UK (Chair)

Cesare Alippi, Politecnico di Milano, Italy (Vice President for Education, IEEE Computational Intelligence Society)

Thomas Bäck, Universiteit Leiden, The Netherlands (Editor, Handbook of Evolutionary Computation)

Piero Bonissone, Formerly Chief Scientist of GE Global Research, USA (WCCI'16 Workshops Chair)

Stefano Cagnoni, Università degli Studi di Parma, Italy (Secretary, AI*IA)

Carlos Coello Coello, CINVESTAV-IPN, Mexico (Associate Editor, IEEE Trans Evolutionary Computation)

Oscar Cordón, Universidad de Granada, Spain (WCCI'16 FUZZ-IEEE Conference Chair)

Kalyanmoy Deb, Michigan State University, USA (Associate Editor, IEEE Trans Evolutionary Computation)

David Fogel, Natural Selection Inc, USA (Founding Editor-in-Chief, IEEE Trans Evolutionary Computation)

Marouane Kessentini, University of Michigan, USA (WCCI'16 CEC Tutorial organiser)

Yuhui Shi, Xi'an Jiaotong-Liverpool University, China (WCCI'16 CEC Technical Chair)

Xin Yao, University of Birmingham, UK (President, IEEE Computational Intelligence Society)

Mengjie Zhang, Victoria University of Wellington, New Zealand (WCCI'16 CEC Special Sessions Chair)

CAutoD principles are discussed in (Downloadable below or from UofG e-library here):

  1. Design of sophisticated fuzzy logic controllers using genetic algorithms, First IEEE World Congress Computational Intelligence, 1994, Ng, K.C., & Li, Y.

  2. Artificial Evolution of neural networks and its application to feedback control, Artificial Intelligence in Engineering, 1996, Li, Y., & Haussler, A.

  3. Genetic algorithm automated approach to the design of sliding mode control systems, Int J Control, 1996, Li, Y. et al

  4. GA automated design and synthesis of analog circuits with practical constraints, IEEE CEC Evolutionary Computation, 2001, Goh, C. (UofG Singapore) and Li, Y.

  5. CAutoCSD - Evolutionary search and optimisation enabled computer automated control system design, Int J Automation and Computing, 2004, Li, Y., et al

  6. IEEE Xplore Proc. 21st IEEE International Conference Automation & Computing - Special Session and Forum on Industry 4.0, Y Li & H Yu, Eds., Sept 2015, Glasgow

  7. IEEE Xplore proceedings of 90 papers on Genetic Algorithms in Engineering Systems: Innovations and Applications, Peter Fleming, Yun Li, et al, Eds., 1997, Glasgow

  8. Special Issues of 23 latest articles in one pdf: Computational Intelligence Approaches to Robotics, Automation, and Control, Yi Chen, Yun Li, et al, Eds., 2015, 302pp, in Mathematical Problems in Engineering

  9. Parallel Processing in a Control Systems Environment, E Rogers & Y Li, Prentice Hall Series on Systems and Control Engineering, 1993, 364pp, ISBN 0-13-651530-4.

  10. Real-World Applications of Evolutionary Computing, S Cagnoni, R Poli, & Y Li, et al, Springer-Verlag Lecture Notes in Computer Science, 2000, 396pp, ISBN 978-3-540-67353-8.

Recently published articles for downloading from UofG e-library:

  1. Cash flow forecast for South African firms. Review of Development Finance, 2015, 5(1), pp.24-33, Y Li, et al

  2. Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys2015, 47(4), No.63, Z Zhan, et al

  3. Distributed evolutionary algorithms and their models: a survey of the state-of-the-art. Applied Soft Computing, 2015, 34(9), pp.286-300, Y Gong, et al

  4. Differential evolution with an evolution path: a DEEP evolutionary algorithm. IEEE Transactions on Cybernetics, 2015, 45(9). pp.1798-1810, Y Li, et al

  5. An evolutionary algorithm with double-level archives for multiobjective optimization. IEEE Transactions on Cybernetics, 2014, 45(9, pp.1851-1863, W Chen, et al

  6. Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks. IEEE Transactions on Industrial Electronics, 2014, 61(12), pp. 7141-7151, M Shen, et al

Related evolutionary computing publications (Downloadable below or from UofG e-library here):

  1. Structural system identification using Genetic Programming and a block diagram tool, Electronics Letters, 1996, Gray, G., et al

  2. Nonlinear model structure identification using Genetic Programming, IFAC J Control Engineering Practice, 1998, Gray, G., et al

    genetic programming for structural optimisation

  3. Evolutionary linearisation in the frequency domain, Electronics Letters, 1996, Tan, K.C. (National University of Singapore), et al

  4. Evolutionary system identification in the time domain, IMechE J of Systems & Control Engineering, 1997, Tan, K.C., & Li, Y.

  5. Evolutionary Computation Meets Machine Learning: A Survey, IEEE Computational Intelligence Magazine, 2011, Zhan, Z., et al

  6. Grey-box model identification via Evolutionary computing, IFAC J Control Engineering Practice, 2002, Tan, K.C., & Li, Y.

  7. A differential evolution algorithm with dual populations for solving periodic railway timetable scheduling problem. IEEE Trans Evolutionary Computation, 2013, J Zhong, et al

  8. Differential evolution with an evolution path: A DEEP evolutionary algorithm, IEEE Trans Cybernetics, 2015, Li, YL., et al


  9. Particle Swarm Optimization with an aging leader and challengers, IEEE Trans Evolutionary Computation, 2013, W Chen, et al

  10. Bi-velocity discrete Particle Swarm Optimization and application..., IEEE Trans Industrial Electronics, 2014

  11. Orthogonal Learning Particle Swarm Optimization, IEEE Trans Evolutionary Computation, 2010

  12. Adaptive Particle Swarm Optimization, IEEE Trans Cybernetics, 2009, Zhang, J. (Sun Yat-sen University), et al

    YL image

  13. Ant Colony Optimization for Wireless Sensor Networks..., IEEE Trans Systems, Man, and Cybernetics, 2011

  14. An efficient Ant Colony system based on receding horizon..., IEEE Trans Intelligent Transportation Systems, 2010

  15. Orthogonal methods based Ant Colony search for solving continuous optimization..., J Computer Science & Technology, 2008

  16. SamACO: variable sampling Ant Colony algorithm for continuous optimization..., IEEE Trans Systems, Man, and Cybernetics, 2010

  17. Protein folding in hydrophobic-polar lattice model: flexible Ant Colony optimization..., Protein and Peptide Letters, 2008


  18. PIDeasy: Patents, software and hardware for PID control: the current art, IEEE Control Systems Magazine, 2006

  19. PID control system analysis, design, and technology, the most popular paper in IEEE Trans Control Systems Tech every month since 2005

    YL image

More published articles can be downloaded via:

Google Scholar


Web of Science (SCI)Papers cited on SCI

ORCID logo


    Yun Li's citations

More on the CAutoD principles

PhD opportunities

PhD is about making an original contribution to knowledge in your field of research, with evidence of systematic study and independent, critical and original thinking.  University of Glasgow has a tradition of original thinking and pushing the boundary of knowledge - our illustrious forebears include James Watt (瓦特), whose 'engineering' (the steam engine with a flyball governor - the world's first industrial feedback controller) at the University in 1769 kicked off the Industrial Revolution, Lord Kelvin (绝对温标 开尔文), Macquorn Rankine (热力学之父 朗肯), John Logie Baird (电视发明者 贝尔德), and Adam Smith (经济学之父 亚当.斯密).  We aim to create technology and knowledge, not just to master it.

Industry 4.0 / Industrie 4.0 / 工业4.0

(Click here and then "Supervision" for example projects. Past PhD students include Kay Chen Tan (Fellow of IEEE) of National University of Singapore, Cindy Goh of University of Glasgow Singapore, and many others, who enjoy a very successful career in academia, industry and commerce around the world.)

Yun Li is Professor of Systems Engineering at University of Glasgow. He received his PhD in parallel computing and control from University of Strathclyde in 1990. Following a period as Consultant Engineer with U.K. National Engineering Laboratory in 1989 and as post-doctoral Research Engineer with Industrial Systems and Control Ltd in 1990, he joined University of Glasgow as Lecturer in 1991. Since 1992, he has trained over 30 PhDs, one of whom has been honoured IEEE Fellow. Dr Li developed a "Neural and Evolutionary Computing" course in 1995 and a popular online interactive courseware GA Demo in 1997.  In 1998, he established and chaired the IEEE CACSD Evolutionary Computation Working Group. He also established the European Network of Excellence in Evolutionary Computing (EvoNet) Workgroup on systems, control and drives, and served on the EvoNet Management Board. In 2011, Professor Li went to Singapore as Founding Director to establish and lead University of Glasgow Singapore, the first overseas subsidiary in the University's 560-year history. Later, he acted as interim/founding director of the University of Glasgow-University of Electronic Science and Technology of China joint education programme in Chengdu. He has over 200 publications, one of which is elected by Thomson Reuters into "Research Front in Computer Science", one into "Research Front in Engineering", four into "Essential Science Indicators" (ESI), one has been the most popular paper in IEEE Transactions on Control Systems Technology every month since publication in 2005, and another has been the IEEE SMC Society's most cited article among all their publications since its publication in 2009. Professor Li is also the University's third "Top Author", an Associate Editor of IEEE Trans Evolutionary Computation and other journals, a Chartered Engineer in the U.K., and an overseas referee for China's "Yangtze River Scholars Program" (教育部长江学者海外评委) and Singapore's National Research Foundation and Ministry of Education. (李耘中文网页, 世界百强格拉斯哥大学简介