 Computerautomated design

Design Automation usually refers to electronic design automation. Extending ComputerAided Design (CAD), automated design and ComputerAutomated Design (CAutoD) ^{[1]}^{[2]}^{[3]} are more concerned with a broader range of applications, such as automotive engineering, civil engineering ^{[4]}^{[5]}^{[6]}^{[7]}, composite material design, control engineering ^{[8]}, dynamic system identification ^{[9]}, financial systems, industrial equipment, mechatronic systems, steel construction ^{[10]}, structural optimisation, and the invention of novel systems.
The concept of CAutoD perhaps first appeared in 1963, in the IBM Journal of Research and Development ^{[1]}, where a computer program was written (1) to search for logic circuits having certain constraints on hardware design and (2) to evaluate these logics in terms of their discriminating ability over samples of the character set they are expected to recognize. More recently, traditional CAD simulation is seen to be transformed to CAutoD by biologicallyinspired machine learning or search techniques such as evolutionary computation^{[11]}^{[12]}, including swarm intelligence algorithms^{[3]}.
Contents
Guiding designs by performance improvements
To meet the ever growing demand of quality and competitiveness, iterative physical prototyping is now often replaced by 'digital prototyping' of a 'good design', which aims to meet multiple objectives such as maximised output, energy efficiency, highest speed and costeffectiveness. The design problem concerns both finding the best design within a known range (i.e., through 'learning' or 'optimisation') and finding a new and better design beyond the existing ones (i.e., through creation and invention). This is equivalent to a search problem in an, almost certainly, multidimensional (multivariate), multimodal space with a single (or weighted) objective or multiple objectives.
Normalized objective function: cost vs. fitness
Using singleobjective CAutoD as an example, if the objective function, either as a cost function , or inversely, as a fitness function , where
 ,
is differentiable under practical constraints in the multidimensional space, the design problem may be solved analytically. Finding the parameter sets that result in a zero firstorder derivative and that satisfy the secondorder derivative conditions would reveal all local optima. Then comparing the values of the performance index of all the local optima, together with those of all boundary parameter sets, would lead to the global optimum, whose corresponding 'parameter' set will thus represent the best design. However, in practice, the optimization usually involves multiple objectgives and the matters involving derivatives are lot more complex.
Dealing with practical objectives
In practice, the objective value may be noisy or even nonnumerical, and hence its gradient information may be unreliable or unavailable. This is particularly true when the problem is multiobjective. At present, many designs and refinements are mainly made through a manual trialanderror process with the help of a CAD simulation package. Usually, such a posteriori learning or adjustments need to be repeated many times until a ‘satisfactory’ or ‘optimal’ design emerges.
Exhaustive search
In theory, this adjustment process can be automated by computerised search, such as exhaustive search. As this is an exponential algorithm, it may not deliver solutions in practice within a limited period of time.
Search in polynomial time
One approach to virtual engineering and automated design is evolutionary computation such as evolutionary algorithms.
Evolutionary algorithms
To reduce the search time, the biologicallyinspired evolutionary algorithm (EA) can be used instead, which is a (nondeterministic) polynomial algorithm. The EA based multiobjective "search team" can be interfaced with an existing CAD simulation package in a batch mode. The EA encodes the design parameters (encoding being necessary if some parameters are nonnumerical) to refine multiple candidates through parallel and interactive search. In the search process, 'selection' is performed using 'survival of the fittest' a posteriori learning. To obtain the next 'generation' of possible solutions, 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 the human designer.
The EA based optimal designs can start from the designer's existing design database or from an initial generation of candidate designs obtained randomly. A number of finally evolved topperforming candidates will represent several automatically optimized digital prototypes.
There are websites that demonstrate interactive evolutionary algorithms for design. EndlessForms.com allows you to evolve 3D objects online and have them 3D printed. PicBreeder.org allows you to do the same for 2D images.
See also
 Electronic design automation
 Design Automation Conference
 Genetic algorithm (GA) applications  automated design
References
 ^ Kamentsky, L.A., and Liu, C.N. (1963). ComputerAutomated Design of Multifont Print Recognition Logic, IBM Journal of Research and Development, 7(1), p.2
 ^ Brncick, M. (2000). Computer automated design and computer automated manufacture, Phys Med Rehabil Clin N Am, Aug, 11(3), 70113.
 ^ Li, Y., et al. (2004). CAutoCSD  Evolutionary search and optimisation enabled computer automated control system design. International Journal of Automation and Computing, 1(1). pp.7688. ISSN 17518520
 ^ KRAMER, GJE; GRIERSON, DE, (1989) COMPUTER AUTOMATED DESIGN OF STRUCTURES UNDER DYNAMIC LOADS, COMPUTERS & STRUCTURES, 32(2), 313325
 ^ MOHARRAMI, H; GRIERSON, DE, 1993, COMPUTERAUTOMATED DESIGN OF REINFORCEDCONCRETE FRAMEWORKS, JOURNAL OF STRUCTURAL ENGINEERINGASCE, 119(7), 20362058
 ^ XU, L; GRIERSON, DE, (1993) COMPUTERAUTOMATED DESIGN OF SEMIRIGID STEEL FRAMEWORKS, JOURNAL OF STRUCTURAL ENGINEERINGASCE, 119(6), 17401760
 ^ Barsan, GM; Dinsoreanu, M, (1997). Computerautomated design based on structural performance criteria, Mouchel Centenary Conference on Innovation in Civil and Structural Engineering, AUG 1921, CAMBRIDGE ENGLAND, INNOVATION IN CIVIL AND STRUCTURAL ENGINEERING, 167172
 ^ Li, Y., et al. (1996). Genetic algorithm automated approach to design of sliding mode control systems, Int J Control, 63(4), 721739.
 ^ Li, Y., et al. (1995). Automation of Linear and Nonlinear Control Systems Design by Evolutionary Computation, Proc. IFAC Youth Automation Conf., Beijing, China, August 1995, pp.5358.
 ^ Barsan, GM, (1995) Computerautomated design of semirigid steel frameworks according to EUROCODE3, Nordic Steel Construction Conference 95, JUN 1921, 787794
 ^ Gregory S. Hornby (2003). Generative Representations for ComputerAutomated Design Systems, NASA Ames Research Center, Mail Stop 2693, Moffett Field, CA 940351000
 ^ J. Clune and H. Lipson (2011). Evolving threedimensional objects with a generative encoding inspired by developmental biology. Proceedings of the European Conference on Artificial Life. 2011
External links
 An online interactive GA based CAutoD demonstrator. Learn step by step or watch global convergence in 2parameter CAutoD.
Tutorials
Categories: Design
 Computeraided design
 Evolutionary algorithms
 Evolutionary computation
Wikimedia Foundation. 2010.