Evolutionary Computer-Automated Design (CAutoD) and Virtual Prototyping for Industry 4.0
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.
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:
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
See also Evolutionary algorithms in engineering applications, D Dasgupta, Z Michalewicz, eds., Springer.
15. Orthogonal methods based Ant Colony search for solving continuous optimization..., J Computer Science & Technology, 2008
system analysis, design, and technology,
IEEE Trans Control Sys Tech,
2005 (Top paper in this journal
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
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).