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. 

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During 1991-2018, the author (Yun Li) taught at University of Glasgow and wrote this applet for his "Neural and Evolutionary Computing" course in 1997. His Intelligent Systems group research into transforming the passive Computer-Aided Design (CAD) to the pro-active Computer-Automated Design (CAutoD) and machine learning and invention, especially for "Industry 4.0".  With data-driven prospects of computation akin to the human being, CAutoD offers automation functions as services for seamless cyber-physical integration, and is applicable to electronic, electrical, mechanical, control, and biomedical engineering, operations management, financial and economic system modelling and optimisation.

New book just out!  Computational Intelligence Assisted Design framework mobilises computational resources; makes use of multiple Computational Intelligence (CI) algorithms; and reduces computational costs. This book provides examples of real world applications of technology.  Case studies have been used to show the integration of services, cloud, big data technology and space missions. It focuses on computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation.

The implementation framework will enable readers to tackle new problems without difficulty through a few tested MATLAB source codes.  It offers:

  • A tutorial, hands-on based presentation of the material

  • From state-of-art to state-of-practice, the most recent developments in computational intelligence with more elaborate discussions on CI

  • Case study sections on how to perform practical or empirical studies, topics including multi-disciplinary applications (engineering and social science)

For more information or purchase, click here.

PhD and post-doc opportunities with full scholarships exist in the following areas jointly at University of Strathclyde in Glasgow, UK, and Dongguan University of Technology in Songshan Lake District next to Shenzhen, China. Contact i4ailab@outlook.com

Industry 4.0 / Industrie 4.0 / 工业4.0

If you wish to do a PhD in this area, press here. 


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

2.   Artificial Evolution of neural networks and its application to feedback control, Artificial Intelligence in Engineering, 1996

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

4.   GA automated design and synthesis of analog circuits with practical constraints, IEEE CEC Evolutionary Computation

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

6.   IEEE Xplore Proc. 21st IEEE International Conference Automation & Computing - Special Session and Forum on Industry 4.0, 2015, Glasgow

7.   IEEE Xplore proceedings of 90 papers on Genetic Algorithms in Engineering Systems: Innovations and Applications, 1997, Glasgow

8.   Special Issues of 23 latest articles in one pdf: Computational Intelligence Approaches to Robotics, Automation, and Control, 2015

9.     Parallel Processing in a Control Systems Environment, Prentice Hall Series on Systems and Control Engineering, 1993

10.  Real-World Applications of Evolutionary Computing, Springer-Verlag Lecture Notes in Computer Science, 2000

See also Evolutionary algorithms in engineering applications, D Dasgupta, Z Michalewicz, eds., Springer.

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

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

genetic programming for structural optimisation
 

3.     Evolutionary linearisation in the frequency domain, Electronics Letters, 1996

4.     Evolutionary system identification in the time domain, IMechE J of Systems & Control Engineering, 1997

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

6.     Grey-box model identification via Evolutionary computing, IFAC J Control Engineering Practice, 2002

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

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


 

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

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 (Among the Top 5 in this journal)


 

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, IEEE Trans Control Sys Tech, 2005 (Top paper in this journal every month)


 

Recently published articles for downloading from UofG e-library:

1.     Cash flow forecast for South African firms. Review of Development Finance, 2015

2.     Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys, 2015

3.     Distributed evolutionary algorithms and their models: a survey of the state-of-the-art. Applied Soft Computing, 2015

4.     Differential evolution with an evolution path: a DEEP evolutionary algorithm. IEEE Transactions on Cybernetics, 2015

5.     An evolutionary algorithm with double-level archives for multiobjective optimization. IEEE Transactions on Cybernetics, 2014

6.     Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks. IEEE Trans Industrial Electronics, 2014

 


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Papers cited on SCI

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Yun Li's citations


Papers welcome for submission to Energies Special Issue on "Smart Creativity for Manufacturing and Industry 4.0"

Information on WCCI'16 Computational Intelligence for Industry 4.0 Special Session

Information on 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 Bńck, 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, UniversitÓ degli Studi di Parma, Italy (Secretary, AI*IA)

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

Oscar Cordˇn, 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)


Much of the CAutoD material above is excerpted from: Li, Y. (1995) "Neural and Evolutionary Computing" Lecture Notes, University of Glasgow, Glasgow, U.K. The EA Demo was written by Yun Li (李耘)  and his 7-week visiting student Sylvain Marquois (then in 1st year at IRESTE, France) in 1997 (So please don't ask me for the source code - it's long buried under the pile), with an aim to interactively help students and new comers to evolutionary computation (进化计算、演化计算) and, in particular, genetic algorithms (遗传算法).