The genetic algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology design. An overview of the genetic algorithm will first describe the genetics-based representations and operators used in a typical genetic algorithm search. Then, a review of previous research in structural optimization is provided. A discretized design representation, and methods for mapping genetic algorithm “chromosomes” into this representation, is then detailed. Several examples of genetic algorithm-based structural topology optimization are provided: we address the optimization of cantilevered plate topologies, and we investigate methods for optimizing finely-discretized design domains. The genetic algorithm’s ability to find families of highly-fit designs is also examined. Finally, a description of potential future work in genetic algorithm-based structural topology optimization is offered.
Abstract-This paper explores contact heating in microelectromechanical systems (MEMS) switches with contact spot sizes less than 100 nm in diameter. Experiments are conducted to demonstrate that contact heating causes a drop in contact resistance. However, existing theory is shown to over-predict heating for MEMS switch contacts because it does not consider ballistic transport of electrons in the contact. Therefore, we extend the theory and develop a predictive model that shows excellent agreement with the experimental results. It is also observed that mechanical cycling causes an increase in contact resistance. We identify this effect as related to the build-up of an insulating film and demonstrate operational conditions to prevent an increase in contact resistance. The improved understanding of contact behavior gained through our modeling and experiments allows switch performance to be improved.[1424]
The genetic algorithm (GA), an optimization technique based on the theory of natural selection, is applied to structural topology design problems. After reviewing the GA and previous research in structural topology optimization, we describe a binary material/void design representation that is encoded in GA chromosome data structures. This representation is intended to approximate a material continuum as opposed to discrete truss structures. Four examples, showing the broad utility of the approach and representation, are then presented. A ®fth example suggests an alternate representation that allows continuously-variable material density. Concluding discussion suggests recommended uses of the technique and describes ongoing and possible future work. Ó 2000 Elsevier Science S.A. All rights reserved.
Background: To search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated.
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