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A survey on automating configuration and parameterization in evolutionary design exploration

Published online by Cambridge University Press:  07 October 2015

Julian R. Eichhoff  and
Dieter Roller
Julian R. Eichhoff*
Affiliation:
Institute of Computer-Aided Product Development Systems, University of Stuttgart, Stuttgart, Germany
Dieter Roller
Affiliation:
Institute of Computer-Aided Product Development Systems, University of Stuttgart, Stuttgart, Germany
*
Reprint requests to: Julian Eichhoff, Institut für Rechnergestützte Ingenieursysteme, Universität Stuttgart, Universitätsstrasse 38, Stuttgart D-70569, Germany. E-mail: [email protected]
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Abstract

Configuration and parameterization of optimization frameworks for the computational support of design exploration can become an exclusive barrier for the adoption of such systems by engineers. This work addresses the problem of defining the elements that constitute a multiple-objective design optimization problem, that is, design variables, constants, objective functions, and constraint functions. In light of this, contributions are reviewed from the field of evolutionary design optimization with respect to their concrete implementation for design exploration. Machine learning and natural language processing are supposed to facilitate feasible approaches to the support of configuration and parameterization. Hence, the authors further review promising machine learning and natural language processing methods for automatic knowledge elicitation and formalization with respect to their implementation for evolutionary design optimization. These methods come from the fields of product attribute extraction, clustering of design solutions, relationship discovery, computation of objective functions, metamodeling, and design pattern extraction.

Keywords

Conceptual DesignDesign AutomationDesign OptimizationEvolutionary ComputingKnowledge EngineeringMachine Learning
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 333 - 350
Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Altshuller, G. (1999). The Innovation Algorithm: TRIZ, Systematic Innovation and Technical Creativity. Worcester: Technical Innovation Center.Google Scholar
Annicchiarico, W., & Cerrolaza, M. (2001). Structural shape optimization 3D finite-element models based on genetic algorithms and geometric modeling. Finite Elements in Analysis and Design 37(5), 403–415.CrossRefGoogle Scholar
Azid, I.A., Kwan, A.S.K., & Seetharamu, K.N. (2002). An evolutionary approach for layout optimization of a three-dimensional truss. Structural and Multidisciplinary Optimization 24(4), 333–337.CrossRefGoogle Scholar
Bagnall, A.J., Rayward-Smith, V.J., & Whittley, I.M. (2001). The next release problem. Information and Software Technology 43(14), 883–890.CrossRefGoogle Scholar
Bai, H., & Kwong, C.K. (2003). Inexact genetic algorithm approach to target values setting of engineering requirements in QFD. International Journal of Production Research 41(16), 3861–3881.CrossRefGoogle Scholar
Bandaru, S., & Deb, K. (2013). Higher and lower-level knowledge discovery from Pareto-optimal sets. Journal of Global Optimization 57(2), 281–298.CrossRefGoogle Scholar
Bentley, P., & Kumar, S. (1999). Three ways to grow designs: a comparison of evolved embryogenies for a design problem. Proc. 1st Genetic and Evolutionary Computing Conf., GECCO'99. San Francisco, CA: Morgan Kaufmann.Google Scholar
Bentley, P.J., & Wakefield, J.P. (1996). Conceptual evolutionary design by a genetic algorithm. Engineering Design and Automation 2(3), 119–131.Google Scholar
Bhatta, S.R., & Goel, A.K. (1994). Discovery of physical principles from design experiences. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8(2), 113–123.CrossRefGoogle Scholar
Blasi, L., Iuspa, L., & Del Core, G. (2000). Conceptual aircraft design based on a multiconstraint genetic optimizer. Journal of Aircraft 37(2), 351–354.CrossRefGoogle Scholar
Bohnenberger, O., Hesser, J., & Männer, R. (1995). Automatic design of truss structures using evolutionary algorithms. Proc. 1995 IEEE Int. Conf. Evolutionary Computation, ICEC'95. New York: IEEE.Google Scholar
Campbell, M.I., Cagan, J., & Kotovsky, K. (1999). A-Design: an agent-based approach to conceptual design in a dynamic environment. Research in Engineering Design 11(3), 172–192.CrossRefGoogle Scholar
Cerrolaza, M., Annicchiarico, W., & Martinez, M. (2000). Optimization of 2D boundary element models using b-splines and genetic algorithms. Engineering Analysis With Boundary Elements 24(5), 427–440.CrossRefGoogle Scholar
Chabot, R., & Brown, D.C. (1994). Knowledge compilation using constraint inheritance. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8(2), 125–142.CrossRefGoogle Scholar
Chapman, C.D., Saitou, K., & Jakiela, M.J. (1994). Genetic algorithms as an approach to configuration and topology design. Journal of Mechanical Design 116(4), 1005–1012.CrossRefGoogle Scholar
Chen, T.Y., & Chiou, Y.H. (2013). Structural topology optimization using genetic algorithms. Proc. 2013 World Congr. Engineering, WCE'13. Hong Kong: Newswood Limited.Google Scholar
Crossley, W.A. (1999). Optimization for aerospace conceptual design through the use of genetic algorithms. Proc. 1st NASA/DoD Workshop on Evolvable Hardware. Washington, DC: IEEE Computer Society.Google Scholar
Deb, K. (1991). Optimal design of a welded beam via genetic algorithms. AIAA Journal 29(11), 2013–2015.CrossRefGoogle Scholar
Dong, A., & Agogino, A.M. (1997). Text analysis for constructing design representations. In Artificial Intelligence in Design ’96 (Gero, J.S., & Sudweeks, F., Eds.), pp. 21–38. Dordrecht: Kluwer Academic.Google Scholar
Ferguson, S., Kasprzak, E., & Lewis, K. (2009). Designing a family of reconfigurable vehicles using multilevel multidisciplinary design optimization. Structural and Multidisciplinary Optimization 39(2), 171–186.CrossRefGoogle Scholar
Forouraghi, B. (1999). On utility of inductive learning in multiobjective robust design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13(1), 27–36.CrossRefGoogle Scholar
Fujita, K. (2002). Product variety optimization under modular architecture. Computer-Aided Design 34(12), 953–965.CrossRefGoogle Scholar
Gerber, D., & Lin, S.-H.E. (2012). Designing-in performance through parameterization, automation, and evolutionary algorithms: “H.D.S. BEAGLE 1.0.”Proc. 17th Int. Conf. Computer-Aided Architectural Design Research in Asia, CAADRIA'12, pp. 141–150, Chennai, India, April 25–28, 2012.Google Scholar
Gero, J.S., Louis, S.J., & Kundu, S. (1994). Evolutionary learning of novel grammars for design improvement. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8(2), 83–94.CrossRefGoogle Scholar
Ghani, R., Probst, K., Liu, Y., Krema, M., & Fano, A. (2006). Text mining for product attribute extraction. ACM SIGKDD Explorations Newsletter 8(1), 41–48.CrossRefGoogle Scholar
Greer, D., & Ruhe, G. (2004). Software release planning: an evolutionary and iterative approach. Information and Software Technology 46(4), 243–253.CrossRefGoogle Scholar
Grierson, D.E., & Pak, W.H. (1993). Optimal sizing, geometrical and topological design using a genetic algorithm. Structural and Multidisciplinary Optimization 6(3), 151–159.CrossRefGoogle Scholar
Güroğlu, S. (2005). An Evolutionary Methodology for Conceptual Design. Ankara: Middle East Technical University Press.Google Scholar
Hajela, P. (1990). Genetic search—an approach to the nonconvex optimization problem. AIAA Journal 28(7), 1205–1210.CrossRefGoogle Scholar
Hamda, H., Jouve, F., Lutton, E., Schoenauer, M., & Sebag, M. (2002). Compact unstructured representations for evolutionary design. Applied Intelligence 16(2), 139–155.CrossRefGoogle Scholar
Hanna, S. (2007). Inductive machine learning of optimal modular structures: estimating solutions using support vector machines. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(4), 351–366.CrossRefGoogle Scholar
Hassan, R.A., & Crossley, W.A. (2003). Multi-objective optimization of communication satellites with two-branch tournament genetic algorithm. Journal of Spacecraft and Rockets 40(2), 266–272.CrossRefGoogle Scholar
Holland, J.H. (1992). Genetic algorithms. Scientific American 267(1), 66–72.CrossRefGoogle Scholar
Hornby, G.S. (2004). Functional scalability through generative representations: the evolution of table designs. Environment and Planning B: Planning and Design 31(4), 569–587.CrossRefGoogle Scholar
Hutcheson, R.S., Jordan, R.L. Jr., Stone, R.B., Terpenny, J.P., & Chang, X. (2006). Application of a genetic algorithm to concept variant selection. Proc. ASME 2006 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., IDETC/CIE'06. New York: ASME.Google Scholar
Ivezic, N., & Garrett, J.H. Jr. (1998). Machine learning for simulation-based support of early collaborative design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(2), 123–139.CrossRefGoogle Scholar
Jakiela, M.J., Chapman, C., Duda, J., Adewuya, A., & Saitou, K. (2000). Continuum structural topology design with genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186(2–4), 339–356.CrossRefGoogle Scholar
Jármai, K., Snyman, J.A., Farkas, J., & Gondos, G. (2003). Optimal design of a welded I-section frame using four conceptually different optimization algorithms. Structural and Multidisciplinary Optimization 25(1), 54–61.Google Scholar
Jenkins, W.M. (1992). Plane frame optimum design environment based on genetic algorithm. Journal of Structural Engineering 118(11), 3103–3112.CrossRefGoogle Scholar
Jin, Y., & Li, W. (2006). Design concept generation: a hierarchical coevolutionary approach. Journal of Mechanical Design 129(10), 1012–1022.CrossRefGoogle Scholar
Kaveh, A., & Kalatjari, V. (2002). Genetic algorithm for discrete-sizing optimal design of trusses using the force method. International Journal for Numerical Methods in Engineering 55(1), 55–72.CrossRefGoogle Scholar
Keane, A.J., & Brown, S.M. (1996). The design of a satellite boom with enhanced vibration performance using genetic algorithm techniques. Proc. 2nd Conf. Adaptive Computing in Engineering Design and Control, ACEDC'96, pp. 107–113, Plymouth, UK, March 26–28.Google Scholar
Kicinger, R., Arciszewski, T., & De Jong, K. (2005 a). Evolutionary computation and structural design: a survey of the state-of-the-art. Computers and Structures 83(23–24), 1943–1978.CrossRefGoogle Scholar
Kicinger, R., Arciszewski, T., & De Jong, K. (2005 b). Evolutionary design of steel structures in tall buildings. Journal of Computing in Civil Engineering 19(3), 223–238.CrossRefGoogle Scholar
Kicinger, R., Arciszewski, T., & De Jong, K. (2005 c). Parameterized versus generative representations in structural design: an empirical comparison. Proc. 7th Genetic and Evolutionary Computing Conf., GECCO'05. New York: ACM.Google Scholar
Kita, E., & Tanie, H. (1999). Topology and shape optimization of continuum structures using GA and BEM. Structural and Multidisciplinary Optimization 17(2–3), 130–139.CrossRefGoogle Scholar
Koumousis, V.K., & Georgiou, P.G. (1994). Genetic algorithms in discrete optimization of steel truss roofs. Journal of Computing in Civil Engineering 8(3), 309–325.CrossRefGoogle Scholar
Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.Google Scholar
Kwong, C.K., Luo, X.G., & Tang, J.F. (2011). A methodology for optimal product positioning with engineering constraints consideration. International Journal of Production Economics 132(1), 93–100.CrossRefGoogle Scholar
Lewis, P.K., Murray, V.R., & Mattson, C.A. (2011). A design optimization strategy for creating devices that traverse the Pareto frontier over time. Structural and Multidisciplinary Optimization 43(2), 191–204.CrossRefGoogle Scholar
Li, H., & Azarm, S. (2002). An approach for product line design selection under uncertainty and competition. Journal of Mechanical Design 124(3), 385–392.CrossRefGoogle Scholar
Li, Z., & Ramani, K. (2007). Ontology-based design information extraction and retrieval. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(2), 137–154.CrossRefGoogle Scholar
Liang, Y., Liu, Y., Kwong, C.K., & Lee, W.B. (2012). Learning the “whys”: discovering design rationale using text mining—an algorithm perspective. Computer-Aided Design 44(10), 916–930.CrossRefGoogle Scholar
Lin, J.-J. (2003). Constructing an intelligent conceptual design system using genetic algorithm. Journal of Materials Processing Technology 140(1–3), 95–99.CrossRefGoogle Scholar
Liu, C.-H. (2010). A group decision-making method with fuzzy set theory and genetic algorithms in quality function deployment. Quality & Quantity 44(6), 1175–1189.CrossRefGoogle Scholar
Mahdavi, S.H., & Hanna, S. (2003). An evolutionary approach to microstructure optimisation of stereolithographic models. Proc. 2003 Congr. Evolutionary Computation, CEC'03. New York: IEEE.Google Scholar
Marler, R.T., & Arora, J.S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26(6), 369–395.CrossRefGoogle Scholar
Matthews, P.C., Standingford, D.W.F., Holden, C.M.E., & Wallace, K.M. (2006). Learning inexpensive parametric design models using an augmented genetic programming technique. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 20(1), 1–18.CrossRefGoogle Scholar
Mehrjerdi, Y.Z. (2010). Quality function deployment and its extensions. International Journal of Quality & Reliability Management 27(6), 616–640.CrossRefGoogle Scholar
Moss, J., Cagan, J., & Kotovsky, K. (2004). Learning from design experience in an agent-based design system. Research in Engineering Design 15(2), 77–92.Google Scholar
Muc, A., & Gurba, W. (2001). Genetic algorithms and finite element analysis in optimization of composite structures. Composite Structures 54(2–3), 275–281.CrossRefGoogle Scholar
Mukerjee, A., & Dabbeeru, M.M. (2012). Grounded discovery of symbols as concept–language pairs. Computer-Aided Design 44(10), 901–915.CrossRefGoogle Scholar
Nakanishi, Y., & Nakagiri, S. (1996). Optimization of frame topology using boundary cycle and genetic algorithm. JSME International Journal Series A: Solid Mechanics and Material Engineering 39(2), 279–285.Google Scholar
Namgoong, H., Crossley, W.A., & Lyrintzis, A.S. (2012). Morphing airfoil design for minimum drag and actuation energy including aerodynamic work. Journal of Aircraft 49(4), 981–990.CrossRefGoogle Scholar
Neocleous, C.C., & Schizas, C.N. (2002). Artificial neural networks in estimating marine propeller cavitation. Proc. 2002 Int. Joint Conf. Neural Networks, IJCNN'02. New York: IEEE.Google Scholar
Object Management Group. (2012). OMG System Modeling Language (OMG SysML™). Needham: Object Management Group.Google Scholar
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K.-H. (2007). Pahl/Beitz Konstruktionslehre: Grundlagen erfolgreicher Produktentwicklung. Methoden und Anwendung, 7th ed.Berlin: Springer.Google Scholar
Parmee, I.C. (1998). Evolutionary and adaptive strategies for efficient search across whole system engineering design hierarchies. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(5), 431–445.CrossRefGoogle Scholar
Parmee, I.C. (2002). Improving problem definition through interactive evolutionary computation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16(3), 185–202.CrossRefGoogle Scholar
Parmee, I.C., & Bonham, C.R. (1999). Cluster-oriented genetic algorithms to support interactive designer/evolutionary computing systems. Proc. 1999 Congr. Evolutionary Computation, CEC'99. New York: IEEE.Google Scholar
Perez, R.E., & Behdinan, K. (2002). Effective multi-mission aircraft conceptual design optimization using a hybrid multi-objective evolutionary method. Proc. 9th AIAA/ISSMO Symp. Multidisciplinary Analysis and Optimization. Reston, VA: AIAA.Google Scholar
Pham, D.T., & Yang, Y. (1993). A genetic algorithm based preliminary design system. Journal of Automobile Engineering 207(2), 127–133.CrossRefGoogle Scholar
Qiu, S.L., Fok, S.C., Chen, C.H., & Xu, S. (2002). Conceptual design using evolution strategy. International Journal of Advanced Manufacturing Technology 20(9), 683–691.CrossRefGoogle Scholar
Rafiq, M.Y., & Rustell, M.J.F. (2011). Building information modelling driven by the evolutionary computing. Proc. 18th European Group for Intelligent Computing in Engineering Workshop, EG-ICE'11, Twente, The Netherlands, July 6–8.Google Scholar
Rajan, S.D. (1995). Sizing, shape, and topology design optimization of trusses using genetic algorithm. Journal of Structural Engineering 121(10), 1480–1487.CrossRefGoogle Scholar
Rajeev, S., & Krishnamoorthy, C.S. (1997). Genetic algorithms-based methodologies for design optimization of trusses. Journal of Structural Engineering 123(3), 350–358.CrossRefGoogle Scholar
Raju, S., Shishtla, P., & Varma, V. (2009). A graph clustering approach to product attribute extraction. Proc. 4th Indian Int. Conf. Artificial Intelligence, IICAI'09, pp. 1438–1447, Tumkur, India, December 16–18.Google Scholar
Reffat, R.M., & Gero, J.S. (2000). Computational situated learning in design. In Artificial Intelligence in Design ’00 (Gero, J.S., Ed.), pp. 589–610. Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
Reich, Y., & Barai, S.V. (1999). Evaluating machine learning models for engineering problems. Artificial Intelligence in Engineering 13(3), 257–272.CrossRefGoogle Scholar
Reich, Y., & Fenves, S.J. (1991). The formation and use of abstract concepts in design. In Concept Formation Knowledge and Experience in Unsupervised Learning (Fisher, D.H. Jr., Pazzani, M.J., & Langley, P., Eds.), pp. 323–353. San Francisco, CA: Morgan Kaufmann.Google Scholar
Ren, Y., & Papalambros, P.Y. (2011). A design preference elicitation query as an optimization process. Journal of Mechanical Design 133(11), 111004-1–111004-9.CrossRefGoogle Scholar
Roth, G.L., & Crossley, W.A. (1998). Commercial transport aircraft conceptual design using a genetic algorithm based approach. Proc. 7th AIAA/USAF/NASA/ISSMO Symp. Multidisciplinary Analysis and Optimization. Reston, VA: AIAA.Google Scholar
Sarkar, S., Dong, A., & Gero, J.S. (2008). Learning symbolic formulations in design optimization. Proc. 3rd Int. Conf. Design Computing and Cognition, DCC’08. Dordrecht: Springer Science+Business Media.Google Scholar
Schnier, T., & Gero, J.S. (1996). Learning genetic representations as alternative to hand-coded shape grammars. In Artificial Intelligence in Design ’96 (Gero, J.S., & Sudweeks, F., Eds.), pp. 39–57. Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
Schoenauer, M. (1996). Shape representations and evolution schemes. Proc. 5th Annual Conf. Evolutionary Programming, Evolutionary Programming. Cambridge, MA: MIT Press.Google Scholar
Schon, D.A. (1992). Designing as reflective conversation with the materials of a design situation. Research in Engineering Design 3(3), 131–147.CrossRefGoogle Scholar
Shang, Y., Huang, K.Z., & Zhang, Q.P. (2009). Genetic model for conceptual design of mechanical products based on functional surface. International Journal of Advanced Manufacturing Technology 42(3–4), 211–221.CrossRefGoogle Scholar
Simpson, T.W. (2004). Product platform design and customization: status and promise. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18(1), 3–20.CrossRefGoogle Scholar
Simpson, T.W., & D'Souza, B.S. (2004). Assessing variable levels of platform commonality within a product family using a multiobjective genetic algorithm. Concurrent Engineering: Research and Applications 12(2), 119–129.CrossRefGoogle Scholar
Skibniewski, M., Arciszewski, T., & Lueprasert, K. (1997). Constructability analysis: machine learning approach. Journal of Computing in Civil Engineering 11(1), 8–16.CrossRefGoogle Scholar
Soh, C.K., & Yang, J. (1996). Fuzzy controlled genetic algorithm search for shape optimization. Journal of Computing in Civil Engineering 10(2), 143–150.CrossRefGoogle Scholar
Soh, C.K., & Yang, Y. (2000). Genetic programming-based approach for structural optimization. Journal of Computing in Civil Engineering 14(1), 31–37.CrossRefGoogle Scholar
Sun, G., & Yao, S. (2012). A framework for an evolutionary computation approach to supporting concept generation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 56(1), 1972–1976.CrossRefGoogle Scholar
Sutcliffe, A., Chang, W.-C., & Neville, R. (2002). Optimizing system requirements with evolutionary computation. Proc. 2002 Congr. Evolutionary Computation, CEC'02. Washington, DC: IEEE Computer Society.Google Scholar
Szczepanik, W., Arciszewski, T., & Wnek, J. (1995). Empirical performance comparison of two symbolic learning systems based on selective and constructive induction. Proc. Workshops at the 14th Int. Joint Conf. Artificial Intelligence, IJCAI'95, pp. 203–214, Montreal, Canada, August 19–25, 1995.Google Scholar
Tai, K., & Akhtar, S. (2005). Structural topology optimization using a genetic algorithm with a morphological geometric representation scheme. Structural and Multidisciplinary Optimization 30(2), 113–127.CrossRefGoogle Scholar
Tang, J., Fung, R.Y.K., Xu, B., & Wang, D. (2002). A new approach to quality function deployment planning with financial consideration. Computers & Operations Research 29(11), 1447–1463.CrossRefGoogle Scholar
Turrin, M., von Buelow, P., & Stouffs, R. (2011). Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Advanced Engineering Informatics 25(4), 656–675.CrossRefGoogle Scholar
Veerappa, V., & Letier, E. (2011). Understanding clusters of optimal solutions in multi-objective decision problems. Proc. 19th IEEE Int. Requirements Engineering Conf., RE'11. Washington, DC: IEEE Computer Society.Google Scholar
Verein Deutscher Ingenieure. (1993). Methodik zum Entwickeln und Konstruieren technischer Systeme und Produkte (VDI 2221). Berlin: Beuth.Google Scholar
Wang, G.G., & Shan, S. (2006). Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design 129(4), 370–380.CrossRefGoogle Scholar
Wang, H., Zhu, X., Wang, H., Hu, S.J., Lin, Z., & Chen, G. (2011). Multi-objective optimization of product variety and manufacturing complexity in mixed-model assembly systems. Journal of Manufacturing Systems 30(1), 16–27.CrossRefGoogle Scholar
Wang, L.-F., & Tang, A.-P. (2011). Collaborative optimization design of reinforcement concrete bridge considering aseismic requirements. Proc. 2011 Int. Conf. Electric Technology and Civil Engineering, ICETCE'11. New York: IEEE.Google Scholar
Wang, S.Y., & Tai, K. (2005). Structural topology design optimization using genetic algorithms with a bit-array representation. Computer Methods in Applied Mechanics and Engineering 194(36–38), 3749–3770.CrossRefGoogle Scholar
Wong, T.-L., Bing, L., & Lam, W. (2011). Normalizing web product attributes and discovering domain ontology with minimal effort. Proc. 4th ACM Int. Conf. Web Search and Data Mining, WSDM'11. New York: ACM.Google Scholar
Wong, T.-L., Lam, W., & Wong, T.-S. (2008). An unsupervised framework for extracting and normalizing product attributes from multiple web sites. Proc. 31st Annual Int. ACM SIGIR Conf. Research and Development in Information Retrieval, SIGIR'08. New York: ACM.Google Scholar
Woon, S.Y., Querin, O.M., & Steven, G.P. (2001). Structural application of a shape optimization method based on a genetic algorithm. Structural and Multidisciplinary Optimization 22(1), 57–64.CrossRefGoogle Scholar
Wu, B., Cheng, X., Wang, Y., Guo, Y., & Song, L. (2009). Simultaneous product attribute name and value extraction from web pages. Proc. 2009 IEEE/WIC/ACM Int. Joint Conf. Web Intelligence and Intelligent Agent Technology, WI-IAT'09. Washington, DC: IEEE Computer Society.Google Scholar
Wu, Z., Campbell, M.I., & Fernández, B.R. (2008). Bond graph based automated modeling for computer-aided design of dynamic systems. Journal of Mechanical Design 130(4), 041102–041102-11.CrossRefGoogle Scholar
Xu, Q.L., Ong, S.K., & Nee, A.Y.C. (2006). Function-based design synthesis approach to design reuse. Research in Engineering Design 17(1), 27–44.CrossRefGoogle Scholar
Yang, M.C., Wood, W.H. III, & Cutkosky, M.R. (2005). Design information retrieval: a thesauri-based approach for reuse of informal design information. Engineering With Computers 21(2), 177–192.CrossRefGoogle Scholar
Yang, Z., & Chen, Y. (2014). Fuzzy optimization modeling approach for QFD-based new product design. Journal of Industrial Engineering 2014, 1–8.CrossRefGoogle Scholar
Yang, Y., & Soh, C.K. (2002). Automated optimum design of structures using genetic programming. Computers and Structures 80(18–19), 1537–1546.CrossRefGoogle Scholar
Yogev, O., Shapiro, A.A., Member, S., & Antonsson, E.K. (2010). Computational evolutionary embryogeny. IEEE Transactions on Evolutionary Computation 14(2), 301–325.CrossRefGoogle Scholar
Zhang, Y., Finkelstein, A., & Harman, M. (2008). Search based requirements optimisation: existing work and challenges. In Requirements Engineering: Foundation for Software Quality, 14th Int. Working Conf., REFSQ'08 (Paech, B., & Rolland, C., Eds.), LNCS Vol. 5025, pp. 88–94. Berlin: Springer.CrossRefGoogle Scholar
Zhang, Y., Harman, M., & Mansouri, S.A. (2007). The multi-objective next release problem. Proc. 9th Genetic and Evolutionary Computing Conf., GECCO'07. New York: ACM.Google Scholar