Complexity measures as a basis for mass customisation of novel designs
Inhaltsverzeichnis
Reference
Gero, JS and Sosa, R (2008): Complexity measures as a basis for mass customisation of novel designs. Environment and Planning B: Planning and Design.
DOI
Abstract
This paper presents a computational approach to the integration of mass customization into the design of novel solutions. It proposes the use of entropy as a function of the diversity of solutions that can be generated within a design space. This work demonstrates that the potential of design systems to generate novel solutions can be estimated using complexity measures. This principle is implemented in an evolutionary system for the design of automotive instrument panels that display situation-relevant information in configurations that adapt to traffic conditions and driving actions. This sample application shows that applying complexity maximization as a selection criterion in evolutionary design systems yields a large variety of solutions of high fitness. The paper also presents guidelines for future developments.
Extended Abstract
Bibtex
Used References
Aelion V, Cagan J and Powers G, 1992, "Input variable expansion - an algorithmic design generation technique", Research in Engineering Design, 4(2), 101-114.
Bentley P, 1999, Evolutionary Design by Computers, Morgan Kaufman, San Francisco.
Boden MA, 1994, Dimensions of Creativity, MIT Press, Cambridge.
Braha D and Maimon O, 1997, "The design process: Properties, paradigms, and structure", IEEE Transactions on Systems, Man & Cybernetics: Part A, 27(2), 146.
Broekhuizen T and Alsem K, 2002, "Success factors for mass customization: a conceptual model", Journal of Market-Focused Management, 5(3), 309-330.
Campbell JL, Carney C and Kantowitz BH, 1998, Human Factors Design Guidelines for Advanced Traveler Information Systems (ATIS), Federal Highway Administration, Department of Transportation USA,
Coyne R, Rosenman MA, Radford AD, Balachandran MB and Gero JS, 1990, Knowledge-Based Design Systems, Addison-Wesley, Reading.
Edmonds B, 1999, Syntactic Measures of Complexity, PhD Thesis, University of Manchester, UK.
Farhang-Mehr A and Azarm S, 2002, "Entropy based multiobjective genetic algorithm for design optimization", Structural and Multidisciplinary Optimization, 24(5), 351-361.
Gavrilova T and Voinov A, 1998, "User-centered design of adaptive interfaces for digital libraries", in C Nikolaou and C Stephanidis (eds.), Research and Advanced Technology for Digital Libraries, Springer, New York, pp. 693-695.
Gero JS, 1990, "Design prototypes. A knowledge representation schema for design", AI Magazine, 11(4), 26-36.
Gero JS, 2000, "Computational models of innovative and creative design processes", Technological Forecasting and Social Change, 64(2-3), 183-196.
Gero JS and Kazakov VA, 1999, Using analogy to extend the behaviour state space in creative design, in JS Gero and ML Maher (eds.) Computational Models of Creative Design IV, Key Centre of Design Computing and Cognition, University of Sydney, Australia, pp. 113-143.
Gero JS and Kazakov VA, 2001, "A genetic engineering extension to genetic algorithms", Evolutionary Systems, 9(1), 71-92.
Gilks WR, Richardson S and Spiegelhalter DJ, 1996, Markov Chain Monte Carlo in Practice, Chapman & Hall, London.
Goldberg DE, 2002, The Design of Innovation: Lessons from and for Competent Genetic Algorithms, Kluwer Academic, Boston.
Hale M 2004, JSci - A science API for Java, http://jsci.sourceforge.net/ accessed on 12/2004.
Hoschek W, 2002, Colt 1.0.3 Distribution: Open Source Libraries for High Performance Scientific and Technical Computing in Java, http://cern.ch/hoschek/colt accessed on 08/2004.
Jiao J and Tseng MM, 1999, "A methodology of developing product family architecture for mass customization", Journal of Intelligent Manufacturing, 10(1), 3-20.
Kalay YE and Carrara G, 1994, Knowledge-Based Computer-Aided Architectural Design, Elsevier, Amsterdam.
Martinez WL and Martinez AR, 2002, Computational statistics handbook with MATLAB, Chapman & Hall/CRC, Boca Raton.
Navinchandra D, 1991, Exploration and Innovation in Design: Towards a Computational Model, Springer-Verlag, New York.
Qian L and Gero JS, 1995, An approach to design exploration using analogy, in JS Gero and ML Maher (eds.) Computational Models of Creative Design, University of Sydney, Key Centre of Design Computing, pp. 3-36.
Rotstan N, 2004, JGAP: A genetic algorithms package in Java, http://jgap.sourceforge.net/ accessed on 12/2004.
Sareni B and Laurent K, 1998, "Fitness sharing and niching methods revisited", IEEE Transactions on Evolutionary Computation, 2(3), 97-106.
Saunders R and Gero JS, 2002, "How to study artificial creativity", Proceedings of the Fourth Conference on Creativity and Cognition, ACM Press, Loughborough, UK, pp. 80-87.
Shannon C, 1948, "A mathematical theory of communications", Bell Systems Tech, 27, 379-423.
Siemens VDO, 2002, Siemens webzine Future Spring 2002: Personalized cars, http://w4.siemens.de/FuI/en/archiv/pof/heft1_02/artikel11/index.html accessed on 12/2004.
Sosa R and Gero JS, 2004, "A computational framework for the study of creativity and innovation in design: Effects of social ties", in JS Gero (ed.) Design Computing and Cognition '04, Kluwer Academic Publishers, Dordrecht, pp. 499-517.
Stiny G, 1993, "Emergence and continuity in shape grammars", in S van Wyk and U Flemming (eds.) CAAD Futures 1993, Springer, pp. 37-54.
Svensson C and Barford A, 2002, "Limits and opportunities in mass customization for 'build to order' SMEs", Computers in Industry, 49(1), 77-89.
Thierens D, 1997, "Selection schemes, elitist recombination and selection intensity", in T Bäck (ed.) Proceedings of the 7th International Conference on Genetic Algorithms ICGA-97, Morgan Kaufmann, pp. 152-159.
Links
Full Text
http://mason.gmu.edu/~jgero/publications/2008/08GeroSosaEnvPlanB.pdf
