CAutoD breaks a 'design' problem down to an 'analysis' or 'simulation' problem and then automates such 'digital prototyping' by intelligent search via biologically-inspired machine learning. An intelligent system utilises computational intelligence to analyse interactions between variables or phenomena, so as to identify causes, effects, drivers and dynamics for their modelling, design and control in a holistic manner. The main tool here is evolutionary computation, including genetic algorithms and swarm intelligence, downloadable from:
CAutoD: CAutoCSD - Evolutionary search and optimisation enabled computer automated control system design, Int J Automation and Computing, 2004
Genetic Algorithm automated approach to the design of sliding mode control systems: Int J Control, 1996, Li, Y. (University of Glasgow), et al
GA automated design and synthesis of analog circuits with practical constraints, Evolutionary Computation, 2001, Goh, C. (UoG Singapore) and Li, Y.
Design of sophisticated fuzzy logic controllers using Genetic Algorithms, First IEEE World Congress on Computational Intelligence, 1994
Evolutionary Computation Meets Machine Learning: A Survey, IEEE Computational Intelligence Magazine, 2011
Evolutionary linearisation in the frequency domain, Electronics Letters, 1996, Tan, K.C. (National University of Singapore), et al
Evolutionary system identification in the time domain, IMechE J of Systems & Control Engineering, 1997
Grey-box model identification via Evolutionary computing, IFAC J Control Engineering Practice, 2002
Artificial Evolution of neural networks and its application to feedback control, Artificial Intelligence in Engineering, 1996
Structural system identification using Genetic Programming and a block diagram oriented simulation tool: Electronics Letters, 1996
Nonlinear model structure identification using Genetic Programming, IFAC J Control Engineering Practice, 1998
Adaptive Particle Swarm Optimization. IEEE Trans Systems, Man, and Cybernetics, 2009, Zhang, J. (Sun Yat-sen University), et al
Orthogonal learning Particle Swarm Optimization. IEEE Trans Evolutionary Computation, 2010
Orthogonal methods based Ant Colony search for solving continuous optimization problems, J Computer Science & Technology, 2008
An efficient Ant Colony system based on receding horizon control for the aircraft arrival sequencing and scheduling... IEEE Trans Intelligent Transportation Syst, 2010
SamACO: variable sampling Ant Colony Optimization algorithm for continuous optimization. IEEE Trans Systems, Man, and Cybernetics, 2010
An Ant Colony Optimization Approach for Maximizing the Lifetime of Heterogeneous Wireless Sensor Networks, IEEE Trans Systems, Man, and Cybernetics, 2011
Protein folding in hydrophobic-polar lattice model: A flexible ant-colony optimization approach, PROTEIN AND PEPTIDE LETTERS, 2008
PIDeasy: PID control system analysis, design, and technology, IEEE Trans Control Systems Technology, 2005
Patents, software and hardware for PID control: an overview and analysis of the current art, IEEE Control Systems Magazine, 2006
Glasgow Genetic Algorithm Demonstrator
run EA_Demo, you'll need
to install latest JavaTM:
|Other papers from||
To meet the ever growing demand in quality and competitiveness, a 'good design' of a system or product needs to meet multiple objectives or design criteria such as maximal performance, highest speeds, reliability, energy efficiency, shortest time-to-market, cost-effectiveness and manufacturability, etc. A design problem is concerned with finding the best parameters within a known or given range through parametric 'optimisation' or 'learning', and is also concerned with inventing a new structure beyond existing designs through structural creation or machine-invention. If the objective cost function JÎ[0 ∞) (or, inversely, the 'fitness function' f = 1/(1+J) Î (0 1] ) is differentiable under practical design constraints, the problem is solved analytically. Unfortunately, this scenario does not usually exist in practice and the problem is hence often unsolvable.
Numerically, this is often known as an 'NP-hard problem' in computing science, but if the design structure and its parameters are pre-set, a candidate design can always be 'analysed' via computer simulations nowadays. Hence, the design problem can be converted to a multidimensional (multivariate) and multi-modal search problem for a combinational design objective or multiple objectives. At present, many designs and refinements are made through manual trial-and-error learning based on the results of a CAD simulation package. Usually, such a process needs to repeat many times until a 'satisfactory' or 'optimal' design emerges.
Such an adjustment process can, however, be performed by biologically-inspired intelligent search, leading to optimal solutions within a polynomial time. For example, an evolutionary algorithm (EA) may be interfaced with the existing CAD package in a batch mode, forming computer-automated design (CAutoD). A typical EA is the genetic algorithm, which encodes parameters (as well as the structure) of each candidate design in a numerical string, known as an artificial 'chromosome', and starts with multiple search points, known as the initial 'population'. The EA varies multiple chromosomes in the population so as to search in parallel, the process of which is guided by automated a-posteriori learning.
This process of artificial evolution selects better performing candidates using a 'survival-of-the-fittest' guideline. To bread the next 'generation' of candidate designs or 'digital prototypes', some parameter values are exchanged between two candidates by an operation called 'crossover' and new values introduced by an operation called 'mutation'. This way, the evolutionary technique makes use of past trial information in a similarly 'intelligent' manner to a human designer. A number of finally 'evolved' top-performing candidate prototypes will present multiple optimal designs. The EA based CAutoD can start from the designer's existing database and/or randomly generated candidates.
The following classification illustrates the complexity of computational problems, where NP-complete are a class of computational problems that cannot be solved in deterministic polynomial (P) time but can be solved in nondeterministic polynomial (NP) time. Given the modern computer simulation power, many virtual engineering problems can now be solved via digital prototyping, although in exponential time (i.e., they may be theoretically solvable but practically intractable). The power of evolutionary computation lies in its ability to solve many exponential problems in NP time, i.e., to make exponential problems practically solvable. Some problems such as winning the lottery, however, remain an exponential problem (i.e., is 'exponential-complete' and can only be solved by enumerations).
algorithm might be the most appropriate to your application at hand:
For full details and a taxonomy of evolutionary algorithms, please refer to "Intelligent Systems and Control MSc" or "Neural and Evolutionary Computing" course notes. For more information on evolutionary algorithms, cf free wikipedia.org.
CAutoD work of Yun Li's Intelligent Systems group started at University of Glasgow in 1992 - I am currently physically in Singapore to head the new University of Glasgow Singapore, but continue to offer PhD projects in Intelligent Systems and CAutoD. It has been a rewarding experience since I started supervising PhD students at University of Glasgow in 1992, mainly to develop evolutionary and machine learning techniques to transform the conventional computer-aided design methodology to the new line of thinking: the biologically-inspired Computer-Automated Design, and to apply it to design-automation, structural optimisation, dynamic system identification and invention of novel systems, engineering or otherwise. Currently, I offer exciting PhD projects in intelligent systems with applications to financial, marketing and business systems, dynamic system modelling and identification, time-series prediction, invention of greener engines, energy systems and smart grid, telecommunication network optimisation, signal processing, bioinformatics, novel circuitry discovery, linear and nonlinear control system design automation, power electronics and drives ... , including but not limited to:
Originality is the key to a PhD - It was James Watt's engineering (devising the world's first industrial feedback controller - the flyball governor for steam engines in 1769) that kicked off the Industrial Revolution. At University of Glasgow, following its heritage of James Watt (瓦特), Lord Kelvin (开尔文), Macquorn Rankine (热力学奠基者 朗肯), John Logie Baird (电视机发明者 贝尔德), as well as Adam Smith (经济学奠基者 亚当•斯密), et al, we create technology and not just discover it. For example, we initiated a multi-disciplinary project even at the undergraduate level as early as 1994 in artificial neural networks for econometrics (collaborating with Political Economy then). So, if you don't discover topics in which you wish to do your PhD, just contact us and we shall try to create a project for you...
Evolutionary Computation at Glasgow: Yun Li's Intelligent Systems Group. The materials on this page were initially provided for use with University of Glasgow's Neural and Evolutionary Computing IV (1995) course. 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).