Download PDF by Zenon Waszczyszyn: Advances of Soft Computing in Engineering

By Zenon Waszczyszyn

ISBN-10: 3211997679

ISBN-13: 9783211997673

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Editors, Next generation intelligent systems in engineering, pages 156–168. EG-ICE, Essen. pl Abstract. This chapter is devoted to applications of biologically inspired methods as evolutionary algorithms and artificial immune systems to optimization and inverse problems, related to size, shape, topology and material optimization and defect identification of structures. 1 Introduction Evolutionary algorithms (Arabas, 2001), (Michalewicz, 1996) are methods which search the space of solutions basing on the analogy to the biological evolution of species.

Evolutionary and adaptive strategies for efficient search across whole system engineering design hierarchies. AIEDAM. Parmee, I. (2001). Evolutionary and adaptive computing in engineering design. Springer-Verlag, 1st edition. , Liepens, G. and Hillyard, M. (1989). Some guidelines for genetic algorithms with penalty functions. In Anon, editor, Proc. 3rd Int. Conf. on genetic algorithms, pages 191–197. Morgan Kaufman, USA. Rourke, J. O. (1998). Computational geometry in C. Cambridge University Press, 2nd edition.

Figure 10. Text Based Design Summary Also, the system provides the best design solution in both graphical and numerical form for each of the short, medium and long span structural systems. The planning grid, the structural grid, the vertical dimensions, overall height of building, weight of steel and total weight of floors are provided, as is the net/gross floor area, the wall/ floor area ratio, the environmental strategy and the fitness. The user is able to edit the design solution, for example moving the position of the cores or adding an atrium.

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Advances of Soft Computing in Engineering by Zenon Waszczyszyn


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