122:
133:' 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
270:
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 objectives and the matters involving derivatives are a lot more complex.
337:. 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, '
269:
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
278:
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
360:
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 finely evolved top-performing candidates will represent several automatically optimized digital prototypes.
656:
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,
364:
There are websites that demonstrate interactive evolutionary algorithms for design. allows you to evolve 3D objects online and have them 3D printed. allows you to do the same for 2D images.
703:
263:
217:
184:
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learning. To obtain the next 'generation' of possible solutions, some parameter values are exchanged between two candidates (by an operation called '
935:
86:
The concept of CAutoD perhaps first appeared in 1963, in the IBM Journal of
Research and Development, where a computer program was written.
841:
Zhang, Jun; Zhan, Zhi-hui; Lin, Ying; Chen, Ni; Gong, Yue-jiao; Zhong, Jing-hui; Chung, Henry S.H.; Li, Yun; Shi, Yu-hui (November 2011).
137:
in an almost certainly, multidimensional (multivariate), multi-modal space with a single (or weighted) objective or multiple objectives.
417:
357:'). This way, the evolutionary technique makes use of past trial information in a similarly intelligent manner to the human designer.
333:
To reduce the search time, the biologically-inspired evolutionary algorithm (EA) can be used instead, which is a (non-deterministic)
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817:
93:
to evaluate these logics in terms of their discriminating ability over samples of the character set they are expected to recognize.
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To meet the ever-growing demand of quality and competitiveness, iterative physical prototyping is now often replaced by '
383:
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learning or adjustments need to be repeated many times until a ‘satisfactory’ or ‘optimal’ design emerges.
760:
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48:
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Li, Yun; Ang, Kiam Heong; Chong, Gregory C. Y.; Feng, Wenyuan; Tan, Kay Chen; Kashiwagi, Hiroshi (2004).
225:
535:
334:
302:
64:
40:
97:
More recently, traditional CAD simulation is seen to be transformed to CAutoD by biologically-inspired
1008:
667:
Li, Yun (1996). "Genetic algorithm automated approach to the design of sliding mode control systems".
151:
36:
1026:
314:
130:
60:
587:
Moharrami, H; Grierson, DE (1993). "Computer-Automated Design of
Reinforced Concrete Frameworks".
500:"CAutoCSD - Evolutionary search and optimisation enabled computer automated control system design"
941:
916:"Evolving three-dimensional objects with a generative encoding inspired by developmental biology"
870:
823:
552:
Kramer, GJE; Grierson, DE (1989). "Computer automated design of structures under dynamic loads".
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704:"Automation of Linear and Nonlinear Control Systems Design by Evolutionary Computation"
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Xu, L; Grierson, DE (1993). "Computer-Automated Design of
Semirigid Steel Frameworks".
134:
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In theory, this adjustment process can be automated by computerised search, such as
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Using single-objective CAutoD as an example, if the objective function, either as a
874:
455:
Brncick, M (2000). "Computer automated design and computer automated manufacture".
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Computer-automated design of semirigid steel frameworks according to EUROCODE-3
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305:, it may not deliver solutions in practice within a limited period of time.
68:
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to search for logic circuits having certain constraints on hardware design
354:
965:
IEEE Transactions on
Systems, Man, and Cybernetics - Part B: Cybernetics
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Computational
Intelligence Assisted Design: In Industrial Revolution 4.0
748:. Nordic Steel Construction Conference 95, June 19-21. pp. 787–794.
433:
17:
1048:
Learn step by step or watch global convergence in 2-parameter CAutoD
761:"Nonlinear model structure identification using genetic programming"
759:
Gray, Gary J.; Murray-Smith, David J.; Li, Yun; et al. (1998).
959:
Zhan, Zhi-Hui; Zhang, Jun; Li, Yun; Chung, Henry Shu-Hung (2009).
890:"Generative Representations for Computer-Automated Design Systems"
120:
47:
are more concerned with a broader range of applications, such as
418:"Computer-Automated Design of Multifont Print Recognition Logic"
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1023:
843:"Evolutionary computation meets machine learning: a survey"
1005:
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ECAL 2011: The 11th
European Conference on Artificial Life
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Genetic algorithm (GA) applications - automated design
353:') and new values introduced (by an operation called '
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228:
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154:
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An online interactive GA based CAutoD demonstrator.
903:(3). Moffett Field, CA: NASA Ames Research Center.
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211:
178:
504:International Journal of Automation and Computing
141:Normalized objective function: cost vs. fitness
702:Li, Yun; Chwee Kim, Ng; Chen Kay, Tan (1995).
8:
117:Guiding designs by performance improvements
235:
227:
186:
153:
847:IEEE Computational Intelligence Magazine
636:10.1061/(ASCE)0733-9445(1993)119:6(1740)
601:10.1061/(ASCE)0733-9445(1993)119:7(2036)
125:Interaction in computer-automated design
422:IBM Journal of Research and Development
405:
961:"Adaptive Particle Swarm Optimization"
83:, and the invention of novel systems.
411:
409:
7:
922:. Vol. 2011. pp. 141–148.
416:Kamentsky, L.A.; Liu, C.-N. (1963).
27:Design Automation usually refers to
258:{\displaystyle f={\tfrac {J}{1+J}}}
170:
45:Computer-Automated Design (CAutoD)
25:
914:Clune, Jeff; Lipson, Hod (2011).
624:Journal of Structural Engineering
589:Journal of Structural Engineering
274:Dealing with practical objectives
669:International Journal of Control
179:{\displaystyle J\in [0,\infty )}
928:10.7551/978-0-262-29714-1-ch024
71:systems, industrial equipment,
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1:
777:10.1016/S0967-0661(98)00087-2
723:10.1016/S1474-6670(17)45158-5
469:10.1016/S1047-9651(18)30806-4
765:Control Engineering Practice
566:10.1016/0045-7949(89)90043-6
384:Design Automation Conference
374:Electronic design automation
43:(CAD), automated design and
29:electronic design automation
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977:10.1109/TSMCB.2009.2015956
888:Gregory S. Hornby (2003).
798:Chen, Yi; Li, Yun (2018).
554:Computers & Structures
457:Phys Med Rehabil Clin N Am
212:{\displaystyle f\in (0,1]}
681:10.1080/00207179608921865
516:10.1007/s11633-004-0076-8
309:Search in polynomial time
1077:Evolutionary computation
711:IFAC Proceedings Volumes
319:evolutionary computation
317:and automated design is
107:evolutionary computation
1072:Evolutionary algorithms
859:10.1109/MCI.2011.942584
343:survival of the fittest
329:Evolutionary algorithms
323:evolutionary algorithms
283:package. Usually, such
279:with the help of a CAD
341:' is performed using '
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101:, including heuristic
49:automotive engineering
1067:Computer-aided design
810:10.1201/9781315153179
303:exponential algorithm
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65:system identification
41:Computer-Aided Design
335:polynomial algorithm
226:
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37:Product Configurator
744:Barsan, GM (1995).
315:virtual engineering
131:digital prototyping
61:control engineering
1029:2021-04-17 at the
1011:2018-11-14 at the
434:10.1147/rd.71.0002
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111:swarm intelligence
77:steel construction
67:and optimization,
57:composite material
937:978-0-262-29714-1
771:(11): 1341–1352.
389:Generative design
379:Design Automation
299:exhaustive search
293:Exhaustive search
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103:search techniques
53:civil engineering
33:Design Automation
16:(Redirected from
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347:a posteriori
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113:algorithms.
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81:optimisation
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39:. Extending
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73:mechatronic
35:which is a
1056:Categories
400:References
281:simulation
63:, dynamic
985:1083-4419
946:267114914
897:Mail Stop
867:1556-603X
828:115518530
785:0967-0661
731:1474-6670
689:0020-7179
644:0733-9445
609:0045-7949
574:0045-7949
524:1751-8520
477:1047-9651
442:0018-8646
351:crossover
339:selection
192:∈
171:∞
159:∈
75:systems,
69:financial
1027:Archived
1009:Archived
993:19362911
532:55417415
485:10989487
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368:See also
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321:such as
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528:S2CID
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