Stochastic optimization of a cold atom experiment using a genetic algorithm

We employ an evolutionary algorithm to automatically optimize different stages of a cold atom experiment without human intervention. This approach closes the loop between computer based experimental control systems and automatic real time analysis and can be applied to a wide range of experimental situations. The genetic algorithm quickly and reliably converges to the most performing parameter set independent of the starting population. Especially in many-dimensional or connected parameter spaces the automatic optimization outperforms a manual search.Comment: 4 pages, 3 figure

Biparental inheritance of plastidial and mitochondrial DNA and hybrid variegation in Pelargonium

Plastidial (pt) and mitochondrial (mt) genes usually show maternal inheritance. Non-Mendelian, biparental inheritance of plastids was first described by Baur (Z Indukt Abstamm Vererbungslehre 1:330–351, 1909) for crosses between Pelargonium cultivars. We have analyzed the inheritance of pt and mtDNA by examining the progeny from reciprocal crosses of Pelargoniumzonale and P. inquinans using nucleotide sequence polymorphisms of selected pt and mt genes. Sequence analysis of the progeny revealed biparental inheritance of both pt and mtDNA. Hybrid plants exhibited variegation: our data demonstrate that the inquinans chloroplasts, but not the zonale chloroplasts bleach out, presumably due to incompatibility of the former with the hybrid nuclear genome. Different distribution of maternal and paternal sequences could be observed in different sectors of the same leaf, in different leaves of the same plant, and in different plants indicating random segregation and sorting-out of maternal and paternal plastids and mitochondria in the hybrids. The substantial transmission of both maternal and paternal mitochondria to the progeny turns Pelargonium into a particular interesting subject for studies on the inheritance, segregation and recombination of mt genes

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E