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Self-regulatory hierarchical coevolution

Published online by Cambridge University Press:  01 November 2003

MIKE ROSENMAN  and
ROB SAUNDERS
MIKE ROSENMAN
Affiliation:
Key Centre of Design Computing and Cognition, School of Architecture, Design Science and Planning, Faculty of Architecture, University of Sydney, Sydney, New South Wales, Australia
ROB SAUNDERS
Affiliation:
Key Centre of Design Computing and Cognition, School of Architecture, Design Science and Planning, Faculty of Architecture, University of Sydney, Sydney, New South Wales, Australia
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Abstract

An evolutionary model for nonroutine design is presented, which is called hierarchical coevolution. The requirements for an evolutionary model of nonroutine design are provided, and some of the problems with existing approaches are discussed. Some of the ways in which these problems have been addressed are examined in terms of the design knowledge required by evolutionary processes. Then, a synthesis of these approaches as a hierarchical coevolutionary model of nonroutine design is presented and the manner in which this model addresses the requirements of an evolutionary design model is discussed. An implementation in the domain of space planning provides an example of a hierarchical design problem.

Keywords

Evolutionary DesignHierarchical CoevolutionNonroutine DesignSelf-Regulation
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 4 , November 2003 , pp. 273 - 285
Copyright
2003 Cambridge University Press

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References

REFERENCES

Bellman, R. (1957). Dynamic Programming. Princeton, NJ: Princeton University Press.
Bentley, P. (Ed.) (1999). Evolutionary Design by Computers. San Francisco, CA: Morgan Kaufmann.
Chen, Z.F. & Brown, D.C. (2002). Explorations of a two-layered A-Design system. Int. Workshop Agents in Design: WAID'02. Cambridge, MA: MIT.
Cohon, J.L. (1978). Multiobjective Programming and Planning. New York: Academic.
Dawkins, R. (1986). The Blind Watchmaker. Harlow, UK: Longman Scientific and Technical.
Fonseca, C.M. & Fleming, P.J. (1995). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1), 1–16.CrossRefGoogle Scholar
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Reading, MA: Addison–Wesley.
Goldberg, D.E. (1999). The race, the hurdle and the sweet spot: lessons from genetic algorithms for the automation of design innovation and creativity. In Evolutionary Design by Computers (Bentley, P.J., Ed.). San Francisco, CA: Morgan Kaufmann.
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.
Jo, J.H. & Gero, J.S. (1995). A genetic approach to space layout planning. Architectural Science Review, 38(1), 37–46.CrossRefGoogle Scholar
Kuziak, A. & Heragu, S. (1987). The facility layout problem. European Journal of Operational Research, 29, 229–251.CrossRefGoogle Scholar
Maher, M.L. & Poon, J. (1996). Modelling design exploration as co-evolution. Microcomputers in Civil Engineering, 11, 195–210.CrossRefGoogle Scholar
Meller, R.D. & Gau, K.-Y. (1996). The facility layout problem: recent trends and perspectives. European Journal of Operational Research, 57, 351–366.CrossRefGoogle Scholar
Moscato, P. (1989). On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Technical Report No. 790. Pasadena, CA: California Institute of Technology, Caltech Concurrent Computation Program.
Potter, M. & De Jong, K.A. (1994). A cooperative coevolutionary approach to function optimization. In Lecture Notes in Computer Science. Proc. Third Conf. Parallel Problem Solving from Nature 2 (Davidor, Y., Schwefel, H.-P. & and Manner, R., Eds.), Vol. 866, pp. 249–257. New York: Springer–Verlag.CrossRef
Rosenman, M.A. (1996). A growth model for form generation using a hierarchical evolutionary approach. Microcomputers in Civil Engineering, 11, 161–172.Google Scholar
Rosenman, M.A. (1997). The generation of form using an evolutionary approach. In Evolutionary Algorithms in Engineering Applications (Dasgupta, D. & Michalewicz, Z., Eds.), pp. 69–85. New York: Springer.CrossRef
Schnecke, V. & Vornberger, O. (1997). Hybrid genetic algorithms for constrained placement problems. IEEE Transactions on Evolutionary Computation, 1(4), 266–277.CrossRefGoogle 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.
Sims, K. (1991). Artificial evolution for computer graphics. Computer Graphics, 25(4), 319–328.CrossRefGoogle Scholar
Todd, S. & Latham, W. (1992). Evolutionary Art and Computers. New York: Academic.
Witbrock, M. & Reilly, S.N. (1999). Evolving genetic art. In Evolutionary Design by Computers (Bentley, P.J., Ed.), pp. 251–259. San Francisco, CA: Morgan Kaufmann.