Constraint graph (layout) - Revision history

en>Hebrides: /* Channel routing */fill out reference using AWB

2015-01-01T13:47:05Z

Channel routing: fill out reference using AWB

← Older revision		Revision as of 15:47, 1 January 2015
Line 1:		Line 1:
	Design Automation usually refers to [[electronic design automation]]. Extending [[Computer-Aided Design]] (CAD), automated design and '''Computer-Automated Design (CAutoD)''' <ref>[http://domino.research.ibm.com/tchjr/journalindex.nsf/0/a5cb0910ea78194885256bfa00683e5a?OpenDocument Kamentsky, L.A., and Liu, C.-N. (1963). Computer-Automated Design of Multifont Print Recognition Logic, IBM Journal of Research and Development, 7(1), p.2]</ref><ref>[http://www.ncbi.nlm.nih.gov/pubmed/10989487 Brncick, M. (2000). Computer automated design and computer automated manufacture, Phys Med Rehabil Clin N Am, Aug, 11(3), 701-13.]</ref><ref>[http://eprints.gla.ac.uk/3818/ Li, Y., et al. (2004). CAutoCSD - Evolutionary search and optimisation enabled computer automated control system design. International Journal of Automation and Computing, 1(1). 76-88. ISSN 1751-8520]</ref> are more concerned with a broader range of applications, such as [[automotive engineering]], [[civil engineering]],<ref>KRAMER, GJE; GRIERSON, DE, (1989) COMPUTER AUTOMATED DESIGN OF STRUCTURES UNDER DYNAMIC LOADS, COMPUTERS & STRUCTURES, 32(2), 313-325</ref><ref>MOHARRAMI, H; GRIERSON, DE, 1993, COMPUTER-AUTOMATED DESIGN OF REINFORCED-CONCRETE FRAMEWORKS, JOURNAL OF STRUCTURAL ENGINEERING-ASCE, 119(7), 2036-2058</ref><ref>XU, L; GRIERSON, DE, (1993) COMPUTER-AUTOMATED DESIGN OF SEMIRIGID STEEL FRAMEWORKS, JOURNAL OF STRUCTURAL ENGINEERING-ASCE, 119(6), 1740-1760</ref><ref>Barsan, GM; Dinsoreanu, M, (1997). Computer-automated design based on structural performance criteria, Mouchel Centenary Conference on Innovation in Civil and Structural Engineering, AUG 19-21, CAMBRIDGE ENGLAND, INNOVATION IN CIVIL AND STRUCTURAL ENGINEERING, 167-172</ref> [[composite material]] design, [[control engineering]],<ref>[http://eprints.gla.ac.uk/51970/ Li, Y., et al. (1996). Genetic algorithm automated approach to design of sliding mode control systems, Int J Control, 63(4), 721-739.]</ref> dynamic [[system identification]],<ref>[http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc94005.ps 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, 53-58.]</ref> [[financial]] systems, industrial equipment, [[mechatronic]] systems, [[steel construction]],<ref>Barsan, GM, (1995) Computer-automated design of semirigid steel frameworks according to EUROCODE-3, Nordic Steel Construction Conference 95, JUN 19-21, 787-794</ref> structural [[optimization (mathematics)\|optimisation]], and the invention of novel systems.

	The concept of CAutoD perhaps first appeared in 1963, in the IBM Journal of Research and Development <sup>[1]</sup>, 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 biologically-inspired [[machine learning]]<ref>[http://eprints.gla.ac.uk/63535/ Zhan, Z.H., et al. (2011). Evolutionary computation meets machine learning: a survey, IEEE Computational Intelligence Magazine, 6(4), 68-75.]</ref> or [[search algorithm\|search techniques]] such as [[evolutionary computation]],<ref>[http://ti.arc.nasa.gov/m/pub-archive/768h/0768%20(Hornby).pdf Gregory S. Hornby (2003). Generative Representations for Computer-Automated Design Systems, NASA Ames Research Center, Mail Stop 269-3, Moffett Field, CA 94035-1000]</ref><ref>[https://www.msu.edu/~jclune/webfiles/publications/2011-CluneLipson-Evolving3DObjectsWithCPPNs-ECAL.pdf J. Clune and H. Lipson (2011). Evolving three-dimensional objects with a generative encoding inspired by developmental biology. Proceedings of the European Conference on Artificial Life. 2011]</ref> including [[swarm intelligence]] algorithms<sup>[3]</sup>.

	~~==Guiding designs by performance improvements==~~		While this mindset is certainly understandable, it simply isn't practical at that price point. A little more than 30 miles, with a 5,200 foot elevation gain, and you'll ride over the finish line. The contemporary bikes for mountains are provided with stronger and lighter frame types in addition to pioneering form and design. Mountain bike frames spend a lot of time on roads these days, too. But except few exceptions; their speed is not as fast as young runners. <br><br>Mountain cycling is some sort of rough sport, so these kind of bicycle parts are crafted accordingly. Now most people wear gloves whilst they are riding because they keep the hands warm. From free shipping on all the bikes across the US, the Road Bike Outlet makes consumers happy anywhere within 1 to 6 days. Ten miles back to the truck is a long walk when pushing 200 pounds of meat on a bike. A smart shopper knows his or her rights, and you should also know this before finalizing any purchase or transaction online. <br><br>Mountain bikes in Arizona have two great rides in Sedona, Highline and the Dry Creek Loop. * Numbness - Impingement of small nerve branches between the second and third or third and fourth toes can cause swelling that results in numbness, tingling, or burning, or sharp shooting pains into the toes. If you have any concerns about in which and how to use [http://djgame.info/profile/ma19l Transfering to mountain bike sizing.], you can get in touch with us at the web site. Please remember the stem lengths can impact just how reactive the bike is. Another way to classify brakes is by mounting style. These are stable forks whose weights are directly in proportion to their durability. <br><br>When discount sales are on, you need to be sure you understand the terms and conditions of your purchase, including warranty information. This article discusses how to go about buying a mountain bike. In the purest sense of the word, a mountain bikes sole mission is to travel "off-road" only. With a Shimano 105 groupset and FSA Gossamer chain set, it's a great choice for under. To help you choose which one you to buy, here are the list of bikes and their use. <br><br>(many commute bikes come with front suspension and disc brakes these days). There is anything from shocks to gears, special wheels, exclusive take care of bars that can be immediately switched to match the terrain you're riding on and so substantially much more. One of the main concerns for any cyclist is safety, having the right kit is a must. The ratio is chosen based on the terrain in which the bike will be ridden, the size of the bike and the strength of the rides. From there you can connect to the west switchback trail simply call "Switchbacks" or "The Old Switchbacks".
	~~[[File:CAutoD~~.~~png\|thumb\|right\|350px\|Interaction in computer-automated design]]~~
	~~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 ~~cost-effectiveness~~. 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), multi-modal space with a single (or weighted) objective or multiple objectives.~~

	~~==Normalized objective function: cost vs. fitness==~~
	~~Using single-objective CAutoD~~ as ~~an example, if the objective function, either~~ as ~~a [[Loss function\|cost function]]~~ <~~math~~>~~J\in[0, \infty)~~<~~/math~~>, ~~or inversely~~, as a ~~[[fitness function]] <math>f\in(0~~,1]<~~/math~~>~~, where~~

	:<~~math~~>~~f = \tfrac{J}{1+J}</math>,~~

	~~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 first-order derivative and that satisfy the second-order 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 non-numerical, and hence its gradient information may be unreliable or unavailable~~. ~~This~~ is ~~particularly true when the problem~~ is ~~multi-objective~~. ~~At present, many designs and refinements~~ are ~~mainly made through a manual trial-and-error 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 algorithm]]s.~~

	~~===Evolutionary algorithms===~~
	~~To reduce~~ the ~~search time,~~ the ~~biologically-inspired evolutionary algorithm (EA) can be used instead~~, ~~which~~ is ~~a (non~~-~~deterministic) [[Exponential algorithm#Polynomial time\|polynomial algorithm]]~~. ~~The EA based multi-objective "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 non-numerical) to refine multiple candidates through parallel~~ and ~~interactive search. In the search process~~, '~~[[natural selection\|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 (genetic algorithm~~)~~\|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 top-performing candidates will represent several automatically optimized digital prototypes.~~

	~~There are websites that demonstrate interactive evolutionary algorithms for design. [http://EndlessForms~~.~~com EndlessForms.com] allows you to evolve 3D objects online and have them 3D printed. [http://www.picbreeder.org PicBreeder.org] allows~~ you to do the ~~same for 2D images.~~

	~~==See also==~~
	~~{{Portal\|Design}}~~
	* [[Electronic design automation]]
	* [[Design Automation Conference]]
	* [[Genetic_algorithm#Applications\|Genetic algorithm (GA) applications - automated design]]

	~~==References==~~
	~~{{Reflist\|2}}~~

	~~==External links==~~
	* [http://userweb.eng.gla.ac.uk/yun.li/ga_demo/ An online interactive GA based CAutoD demonstrator.] Learn step by step or ~~watch global convergence in 2-parameter CAutoD~~
	* [https://www.google.com/search?tbm=isch&q=evolutionary+design Images of practical examples.]

	~~==Tutorials==~~

	~~{{DEFAULTSORT:Computer-Automated Design}}~~
	~~[[Category:Design]]~~
	~~[[Category:Computer-aided design]]~~
	~~[[Category:Evolutionary algorithms]]~~
	~~[[Category:Evolutionary computation]]~~

en>Yobot: WP:CHECKWIKI error 22 fixes (category with space) + general fixes (BRFA 16) using AWB (7799)

2011-07-29T21:28:26Z

WP:CHECKWIKI error 22 fixes (category with space) + general fixes (BRFA 16) using AWB (7799)

New page

Design Automation usually refers to [[electronic design automation]]. Extending [[Computer-Aided Design]] (CAD), automated design and '''Computer-Automated Design (CAutoD)''' <ref>[http://domino.research.ibm.com/tchjr/journalindex.nsf/0/a5cb0910ea78194885256bfa00683e5a?OpenDocument Kamentsky, L.A., and Liu, C.-N. (1963). Computer-Automated Design of Multifont Print Recognition Logic, IBM Journal of Research and Development, 7(1), p.2]</ref><ref>[http://www.ncbi.nlm.nih.gov/pubmed/10989487 Brncick, M. (2000). Computer automated design and computer automated manufacture, Phys Med Rehabil Clin N Am, Aug, 11(3), 701-13.]</ref><ref>[http://eprints.gla.ac.uk/3818/ Li, Y., et al. (2004). CAutoCSD - Evolutionary search and optimisation enabled computer automated control system design. International Journal of Automation and Computing, 1(1). 76-88. ISSN 1751-8520]</ref> are more concerned with a broader range of applications, such as [[automotive engineering]], [[civil engineering]],<ref>KRAMER, GJE; GRIERSON, DE, (1989) COMPUTER AUTOMATED DESIGN OF STRUCTURES UNDER DYNAMIC LOADS, COMPUTERS & STRUCTURES, 32(2), 313-325</ref><ref>MOHARRAMI, H; GRIERSON, DE, 1993, COMPUTER-AUTOMATED DESIGN OF REINFORCED-CONCRETE FRAMEWORKS, JOURNAL OF STRUCTURAL ENGINEERING-ASCE, 119(7), 2036-2058</ref><ref>XU, L; GRIERSON, DE, (1993) COMPUTER-AUTOMATED DESIGN OF SEMIRIGID STEEL FRAMEWORKS, JOURNAL OF STRUCTURAL ENGINEERING-ASCE, 119(6), 1740-1760</ref><ref>Barsan, GM; Dinsoreanu, M, (1997). Computer-automated design based on structural performance criteria, Mouchel Centenary Conference on Innovation in Civil and Structural Engineering, AUG 19-21, CAMBRIDGE ENGLAND, INNOVATION IN CIVIL AND STRUCTURAL ENGINEERING, 167-172</ref> [[composite material]] design, [[control engineering]],<ref>[http://eprints.gla.ac.uk/51970/ Li, Y., et al. (1996). Genetic algorithm automated approach to design of sliding mode control systems, Int J Control, 63(4), 721-739.]</ref> dynamic [[system identification]],<ref>[http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc94005.ps 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, 53-58.]</ref> [[financial]] systems, industrial equipment, [[mechatronic]] systems, [[steel construction]],<ref>Barsan, GM, (1995) Computer-automated design of semirigid steel frameworks according to EUROCODE-3, Nordic Steel Construction Conference 95, JUN 19-21, 787-794</ref> structural [[optimization (mathematics)|optimisation]], and the invention of novel systems.

The concept of CAutoD perhaps first appeared in 1963, in the IBM Journal of Research and Development <sup>[1]</sup>, 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 biologically-inspired [[machine learning]]<ref>[http://eprints.gla.ac.uk/63535/ Zhan, Z.H., et al. (2011). Evolutionary computation meets machine learning: a survey, IEEE Computational Intelligence Magazine, 6(4), 68-75.]</ref> or [[search algorithm|search techniques]] such as [[evolutionary computation]],<ref>[http://ti.arc.nasa.gov/m/pub-archive/768h/0768%20(Hornby).pdf Gregory S. Hornby (2003). Generative Representations for Computer-Automated Design Systems, NASA Ames Research Center, Mail Stop 269-3, Moffett Field, CA 94035-1000]</ref><ref>[https://www.msu.edu/~jclune/webfiles/publications/2011-CluneLipson-Evolving3DObjectsWithCPPNs-ECAL.pdf J. Clune and H. Lipson (2011). Evolving three-dimensional objects with a generative encoding inspired by developmental biology. Proceedings of the European Conference on Artificial Life. 2011]</ref> including [[swarm intelligence]] algorithms<sup>[3]</sup>.

==Guiding designs by performance improvements==
[[File:CAutoD.png|thumb|right|350px|Interaction in computer-automated design]]
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 cost-effectiveness. 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), multi-modal space with a single (or weighted) objective or multiple objectives.

==Normalized objective function: cost vs. fitness==
Using single-objective CAutoD as an example, if the objective function, either as a [[Loss function|cost function]] <math>J\in[0, \infty)</math>, or inversely, as a [[fitness function]] <math>f\in(0,1]</math>, where

:<math>f = \tfrac{J}{1+J}</math>,

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 first-order derivative and that satisfy the second-order 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 non-numerical, and hence its gradient information may be unreliable or unavailable. This is particularly true when the problem is multi-objective. At present, many designs and refinements are mainly made through a manual trial-and-error 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 algorithm]]s.

===Evolutionary algorithms===
To reduce the search time, the biologically-inspired evolutionary algorithm (EA) can be used instead, which is a (non-deterministic) [[Exponential algorithm#Polynomial time|polynomial algorithm]]. The EA based multi-objective "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 non-numerical) to refine multiple candidates through parallel and interactive search. In the search process, '[[natural selection|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 (genetic algorithm)|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 top-performing candidates will represent several automatically optimized digital prototypes.

There are websites that demonstrate interactive evolutionary algorithms for design. [http://EndlessForms.com EndlessForms.com] allows you to evolve 3D objects online and have them 3D printed. [http://www.picbreeder.org PicBreeder.org] allows you to do the same for 2D images.

==See also==
{{Portal|Design}}
* [[Electronic design automation]]
* [[Design Automation Conference]]
* [[Genetic_algorithm#Applications|Genetic algorithm (GA) applications - automated design]]

==References==
{{Reflist|2}}

==External links==
* [http://userweb.eng.gla.ac.uk/yun.li/ga_demo/ An online interactive GA based CAutoD demonstrator.] Learn step by step or watch global convergence in 2-parameter CAutoD
* [https://www.google.com/search?tbm=isch&q=evolutionary+design Images of practical examples.]

==Tutorials==

{{DEFAULTSORT:Computer-Automated Design}}
[[Category:Design]]
[[Category:Computer-aided design]]
[[Category:Evolutionary algorithms]]
[[Category:Evolutionary computation]]