Generic Evolutionary Design

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Reference

P. J. Bentley and J. P. Wakefield: Generic Evolutionary Design. In: Soft Computing in Engineering Design and Manufacturing, pp. 289-298, Springer-Verlag, 23-27 June 1998.

DOI

Abstract

Generic evolutionary design means the creation of a range of different designs by evolution. This paper introduces generic evolutionary design by a computer, describing a system capable of the evolution of a wide range of solid object designs from scratch, using a genetic algorithm. The paper reviews relevant literature, and outlines a number of advances necessitated by the development of the system, including: a new generic representation of solid objects, a new multiobjective fitness ranking method, and variable-length chromosomes. A library of modular evaluation software is also described, which allows a user to define new design problems quickly and easily by picking combinations of modules to guide the evolution of designs.

Finally, the feasibility of generic evolutionary design by a computer is demonstrated by presenting the successful evolution of both conventional and unconventional designs for a range of different solid-object design tasks, e.g. tables, heatsinks, prisms, boat hulls, aerodynamic cars.

Extended Abstract

Bibtex

Used References

[1] Bentley, P. J., 1996, Generic Evolutionary Design of Solid Objects using a Genetic Algorithm. Ph.D. Thesis, University of Huddersfield, Huddersfield, UK.

[2] Bentley, P. J. & Wakefield, J. P., 1996a, The Evolution of Solid Object Designs using Genetic Algorithms. Modern Heuristic Search Methods, John Wiley & Sons Inc., Ch 12, 197-211.

[3] Bentley, P. J. & Wakefield, J. P., 1996b, Generic Representation of Solid Geometry for Genetic Search. Microcomputers in Civil Engineering 11:3, 153-161.

[4] Bentley, P. J. & Wakefield, J. P., 1996c, Hierarchical Crossover in Genetic Algorithms. Proceedings of the 1st On-line Workshop on Soft Computing (WSC1), Nagoya University, Japan, 37-42.

[5] Culley, S. J. and Wallace, A. P., 1994, Optimum Design of Assemblies with Standard Components. Proc. of Adaptive Computing in Engineering Design and Control - ’94, Plymouth, 163-168.

[6] Dawkins, R. 1986, The Blind Watchmaker, Longman Scientific & Technical Pub.

[7] Dyer, M. Flower, M. and Hodges, J., 1986, ’EDISON’: an engineering design invention system operating naively. Artificial Intelligence 1, 36-44.

[8] Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley.

[9] Holland, J. H., 1992, Genetic Algorithms. Scientific American, 66-72.

[10] Parmee, I C & Denham, M J, 1994, The Integration of Adaptive Search Techniques with Current Engineering Design Practice. Proc. of Adaptive Computing in Engineering Design and Control -’94, Plymouth, 1-13.

[11] Pham, D. T. & Yang, Y., 1993, A genetic algorithm based preliminary design system. Journal of Automobile Engineers v207:D2, 127-133.

[12] Rosenman, M. A., 1996, A Growth Model for Form Generation Using a Hierarchical Evolutionary Approach. Microcomputers in Civil Engineering 11:3, 163-174.

[13] Todd, S. & Latham, W., 1992, Evolutionary Art and Computers, Academic Press.

[14] Tong, S.S., 1992, Integration of symbolic and numerical methods for optimizing complex engineering systems. IFIP Transactions (Computer Science and Technology) vA-2, 3-20.

[15] Williams, B. C., 1990, Visualising potential interactions: constructing novel devices fro


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Full Text

http://eprints.hud.ac.uk/4053/1/PB_%26_JPW_1997_Generic_Evolutionary_Design.pdf

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http://eprints.hud.ac.uk/4053/