Download Advances in Formal Design Methods for CAD: Proceedings of by Mary Lou Maher, Josiah Poon, Sylvie Boulanger (auth.), John PDF

By Mary Lou Maher, Josiah Poon, Sylvie Boulanger (auth.), John S. Gero, Fay Sudweeks (eds.)

Designing is among the most important of human acts. unusually, on condition that designing has been taking place for plenty of millenia, our figuring out of the tactics of designing is remarkably restricted. lately, layout equipment were formalised now not as humano-centred tactics yet as approaches in a position to laptop implementation with the objective of augmenting human designers. This quantity comprises contributions which hide layout equipment according to evolutionary structures, generative approaches, review equipment and research tools. It offers the state-of-the-art in formal layout tools for desktop aided design.

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Additional resources for Advances in Formal Design Methods for CAD: Proceedings of the IFIP WG5.2 Workshop on Formal Design Methods for Computer-Aided Design, June 1995

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Our preliminary results show that design exploration can be modelled as computational methods. Our preliminary results also show that the representation of the design domain can result in an unstable method - that is, one that produces a different result for the same problem in two different runs. This instability may be a desirable feature since it provides a computational model that is as unpredictable as human designers. However, we need to test the model further to understand why there is instability and how the genotype, phenotype, and evaluation functions influence the nature of the exploration.

1) Evolution of complex building blocks. For every component of the objective function Fk divide the population into 3 groups: "good" (Fk(X) > maxi=l,M Fk(Xi)- Ag*(maxi=l,M Fk(Xi) -mini=l,M Fk(Xi)), "bad" (Fk(X) < mini=l,M Fk(Xi)+ Ab* (maxi=l,M Fk(Xi) -mini=l,M Fk(Xi)), and "neutral" (the rest of population). . Determine J combinations bn+j = x( {b j , r&, ri, ... ,rL}), j = 1, ... , J of the current building blocks which distinguish the "good" sampling set from the "bad" one statistically significantly using anyone of the pattern recognition algorithms.

The fraction of composite building blocks in the total pool of building blocks used to assemble the population vs. generation number. The objective function has two components: the area of closed holes and the number of connections between holes and the outside space. The initial set of building blocks contains only elementary building blocks. Evolution proceeds until it naturally dies off. elementary components. We generate a new population during the stage (2) of the Algorithm using the modification of the simple genetic algorithm tailored to handle multidimensional objective functions (Gero and Kazakov, 1995).

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