The genetic algorithm (GA) is a popular random search and optimization method inspired by the concepts of crossover, random mutation, and natural selection from evolutionary biology. The real-valued genetic algorithm (RGA) is an improved version of the genetic algorithm designed for direct operation on real-valued variables. In this work, a modified version of a genetic algorithm is introduced, which is called a modified genetic algorithm with micro-movement (MGAM). It implements a particle swarm optimization(PSO)-inspired micro-movement phase that helps to improve the convergence rate, while employing the e'cient GA mechanism for maintaining population diversity. In order to test the capability of the MGAM, we firrst implement it on five generally used test functions. Then we test the MGAM on two typical nonlinear dynamical systems. The performance of the MGAM is compared to a basic RGA on all these applications. Finally, we implement the MGAM on the most important application, which is the plasma physics-based model of the solar wind-driven magnetosphere-ionosphere system (WINDMI). In order to use this model for real-time prediction of geomagnetic activity, the model parameters require up-dating every 6-8 hours. We use the MGAM to train the parameters of the model in order to achieve the lowest mean square error (MSE) against the measured auroral electrojet (AL) and Dst indices. The performance of the MGAM is compared to the RGA on historical geomagnetic storm datasets. While the MGAM performs substantially better than the RGA when evaluating standard test functions, the improvement is about 6-12 percent when used on the 20D nonlinear dynamical WINDMI model.

Leaf rust caused by Puccinia hordei is an important disease of barley (Hordeum vulgare) in many regions of the world. Yield losses up to 62% have been reported in susceptible cultivars. The Rph5 gene confers resistance to the most prevalent races (8 and 30) of barley leaf rust in the United States. Therefore, the molecular mapping of Rph5 is of great interest. Genetic studies were performed by analysis of 93 and 91 F2 plants derived from the crosses 'Bowman' (rph5) x 'Magnif 102' (Rph5) and 'Moore' (rph5) x Virginia 92-42-46 (Rph5), respectively. Linkage analysis positioned the Rph5 locus to the extreme telomeric region of the short arm of barley chromosome 3H at 0.2 cM proximal to RFLP marker VT1 and 0.5 cM distal from RFLP marker C970 in the Bowman x Magnif 102 population. Synteny between rice chromosome 1 and barley chromosome 3 was employed to saturate the region within the sub-centimorgan region around Rph5 using sequence-tagged site (STS) markers that were developed based on barley expressed sequence tags (ESTs) syntenic to the phage (P1)-derived artificial chromosome (PAC) clones comprising distal region of the rice chromosome 1S. Five rice PAC clones were used as queries to blastn 370,258 barley ESTs. Ninety four non-redundant EST sequences were identified from the EST database and used as templates to design 174 pairs of primer combinations. As a result, 10 EST-based STS markers were incorporated into the 'Bowman' x 'Magnif 102' high-resolution map of the Rph5 region. More importantly, six markers, including five EST-derived STS sequences, co-segregate with Rph5. Genes, represented by these markers, are putative candidates for Rph5. Results of this study demonstrate the usefulness of rice genomic resources for efficient deployment of barley EST resources for marker saturation of targeted barley genomic region. / Ph. D.