On the analysis of a dynamic evolutionary algorithm

Evolutionary algorithms are applied as problem-independent optimization algorithms. They are quite efficient in many situations. However, it is difficult to analyze even the behavior of simple variants of evolutionary algorithms like the (1+1) EA on rather simple functions. Nevertheless, only the analysis of the expected run time and the success probability within a given number of steps can guide the choice of the free parameters of the algorithms. Here static (1+1) EAs with a fixed mutation probability are compared with dynamic (1+1) EAs with a simple schedule for the variation of the mutation probability. The dynamic variant is first analyzed for functions typically chosen as example-functions for evolutionary algorithms. Afterwards, it is shown that it can be essential to choose the suitable variant of the (1+1) EA. More precisely, functions are presented where each static (1+1) EA has exponential expected run time while the dynamic variant has polynomial expected run time. For other functions it is shown that the dynamic (1+1) EA has exponential expected run time while a static (1+1) EA with a good choice of the mutation probability has polynomial run time with overwhelming probability.     

Rehabilitation of the Gauss-Jordan algorithm

Summary In this paper a Gauss-Jordan algorithm with column interchanges is presented and analysed. We show that, in contrast with Gaussian elimination, the Gauss-Jordan algorithm has essentially differing properties when using column interchanges instead of row interchanges for improving the numerical stability. For solutions obtained by Gauss-Jordan with column interchanges, a more satisfactory bound for the residual norm can be given. The analysis gives theoretical evidence that the algorithm yields numerical solutions as good as those obtained by Gaussian elimination and that, in most practical situations, the residuals are equally small. This is confirmed by numerical experiments. Moreover, timing experiments on a Cyber 205 vector computer show that the algorithm presented has good vectorisation properties.     

Investigation of organic impurities adsorbed on and incorporated into electroplated copper layers

At room temperature electroplated copper layers exhibit changes in resistivity, residual stress, and microstructure. This process, known as self-annealing, is intimately linked to the release of organic impurities, which stem from the incorporation of organic additives into the Cu layer in the course of the electroplating process. The behavior of these impurities during self-annealing, represented by the carbon content, could be detected by analytical radio frequency glow discharge optical emission spectrometry (GD-OES) and carrier gas hot extraction (CGHE). The precondition of a quantitative determination is a surface cleaning procedure to remove adsorbed organics from the copper surface. It was observed that at first almost all impurities have to leave the Cu metallization before an accelerated abnormal grain growth can start. The small amount of remaining organic species after self-annealing could be quantified by both examination techniques, GD-OES and CGHE.     

Uran im Quellengebiete von Franzensbad

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