University of Lleida

About: University of Lleida is a education organization based out in Lleida, Spain. It is known for research contribution in the topics: Population & Pregnancy. The organization has 2939 authors who have published 5853 publications receiving 148417 citations. The organization is also known as: Escola Superior Politècnica & Universitat de Lleida.

Floret development and grain setting differences between modern durum wheats under contrasting nitrogen availability

Abstract: Wheat yield depends on the number of grains per square metre, which in turn is related to the number of fertile florets at anthesis. The dynamics of floret generation/degeneration were studied in contrasting conditions of nitrogen (N) and water availability of modern, well-adapted, durum wheats in order to understand further the bases for grain number determination. Experiments were carried out during the 2008–2009 and 2009–2010 growing seasons at Lleida (NE Spain). The first experiment involved four cultivars (Claudio, Donduro, Simeto, and Vitron) and two contrasting N availabilities (50 kgN ha–1 and 250 kgN ha–1; N50 and N250) while experiment 2 included the two cultivars most contrasting in grain setting responsiveness to N in experiment 1, and two levels of N (N50 and N250), under irrigated (IR) and rainfed (RF) conditions. In addition, a detillering treatment was imposed on both cultivars under the IR+N250 condition. The number of fertile florets at anthesis was increased by ~30% in response to N fertilization (averaging across treatments and spikelet positions). The effect of N and water availability was evident on floret developmental rates from the third floret primordium onwards, as these florets in the central spikelets of all genotypes reached the stage of a fertile floret in N250 while in N50 they did not. In this study, clear differences were found between the cultivars in their responsiveness to N by producing more fertile florets at anthesis (through accelerating developmental rates of floret primordia), by increasing the likelihood of particular grains to be set, or by both traits.

Model-based genetic algorithms for algorithm configuration

Abstract: Automatic algorithm configurators are important practical tools for improving program performance measures, such as solution time or prediction accuracy. Local search approaches in particular have proven very effective for tuning algorithms. In sequential local search, the use of predictive models has proven beneficial for obtaining good tuning results. We study the use of non-parametric models in the context of population-based algorithm configurators. We introduce a new model designed specifically for the task of predicting high-performance regions in the parameter space. Moreover, we introduce the ideas of genetic engineering of offspring as well as sexual selection of parents. Numerical results show that model-based genetic algorithms significantly improve our ability to effectively configure algorithms automatically.