On generating optimal signal probabilities for random tests: a genetic approach

Genetic Algorithms are robust search and optimization techniques. A Genetic Algorithm based approach for determining the optimal input distributions for generating random test vectors is proposed in the paper. A cost function based on the COP testability measure for determining the efficacy of the input distributions is discussed. A brief overview of Genetic Algorithms (GAs) and the specific details of our implementation are described. Experimental results based on ISCAS-85 benchmark circuits are presented. The performance of our GAbased approach is compared with previous results. While the GA generates more efficient input distributions than the previous methods which are based on gradient descent search, the overheads of the GA in computing the input distributions are larger. To account for the relatively quick convergence of the gradient descent methods, we analyze the landscape of the COP-based cost function. We prove that the cost function is unimodal in the search space. This feature makes the cost function amenable to optimization by gradient-descent techniques as compared to random search methods such as Genetic Algorithms

Interferometric Phase Calibration Sources in the Declination Range 0deg to -30deg

We present a catalog of 321 compact radio sources in the declination range 0deg > delta > -30deg. The positions of these sources have been measured with a two-dimensional rms accuracy of 35 milliarcseconds using the NRAO Very Large Array. Each source has a peak flux density >50 mJy at 8.4 GHz. We intend for this catalog to be used mainly for selection of phase calibration sources for radio interferometers, although compact radio sources have other scientific uses.Comment: 9 pages. To appear in ApJS. Catalog (Table 3) is abbreviated in printed version. Complete catalog available at ftp://ftp.aoc.nrao.edu/pub/staff/jwrobel/WPW2003_ApJS.tx