Genetic control of propagation traits in a single Corymbia torelliana x Corymbia variegata family

Genetic control of vegetative propagation traits was described for a second-generation, outbred, intersectional hybrid family (N = 208) derived from two species, Corymbia torelliana (F. Muell.) K.D. Hill & L.A.S. Johnson and Corymbia variegata (F. Muell.) K.D. Hill & L.A.S. Johnson, which contrast for propagation characteristics and in their capacity to develop lignotubers. Large phenotypic variances were evident for rooting and most other propagation traits, with significant proportions attributable to differences between clones (broad-sense heritabilities 0.2-0.5). Bare root assessment of rooting rate and root quality parameters tended to have the highest heritabilities, whereas rooting percentage based on root emergence from pots and shoot production were intermediate. Root biomass and root initiation had the lowest heritabilities. Strong favourable genetic correlations were found between rooting percentage and root quality traits such as root biomass, volume, and length. Lignotuber development on a seedling was associated with low rooting and a tendency to poor root quality in cuttings and was in accord with the persistence of species parent types due to gametic phase disequilibrium. On average, nodal cuttings rooted more frequently and with higher quality root systems, but significant cutting type x genotype interaction indicated that for some clones, higher rooting rates were obtained from tips. Low germination, survival of seedlings, and rooting rates suggested strong hybrid breakdown in this family.

Genetic control of wheat quality: interactions between chromosomal regions determining protein content and composition, dough rheology, and sponge and dough baking properties

While the genetic control of wheat processing characteristics such as dough rheology is well understood, limited information is available concerning the genetic control of baking parameters, particularly sponge and dough (S&D) baking. In this study, a quantitative trait loci (QTL) analysis was performed using a population of doubled haploid lines derived from a cross between Australian cultivars Kukri x Janz grown at sites across different Australian wheat production zones (Queensland in 2001 and 2002 and Southern and Northern New South Wales in 2003) in order to examine the genetic control of protein content, protein expression, dough rheology and sponge and dough baking performance. The study highlighted the inconsistent genetic control of protein content across the test sites, with only two loci (3A and 7A) showing QTL at three of the five sites. Dough rheology QTL were highly consistent across the 5 sites, with major effects associated with the Glu-B1 and Glu-D1 loci. The Glu-D1 5 + 10 allele had consistent effects on S&D properties across sites; however, there was no evidence for a positive effect of the high dough strength Glu-B1-al allele at Glu-B1. A second locus on 5D had positive effects on S&D baking at three of five sites. This study demonstrated that dough rheology measurements were poor predictors of S&D quality. In the absence of robust predictive tests, high heritability values for S&D demonstrate that direct selection is the current best option for achieving genetic gain in this product category.

Biology, Management and Genetic Stock Structure of Mangrove Jack, (Lutjanus argentimaculatus) in Australia.

This project has contributed to the ecologically sustainable management of mangrove jack in Australia by providing comprehensive information on its biology, habitat requirements, population parameters and stock structure. Specifically, the project has resulted in an enhanced understanding of the life history of Australian mangrove jack, the levels of exploitation in its local fishery and the likely existence of a single genetic stock throughout Queensland.

A physiological framework to explain genetic and environmental regulation of tillering in sorghum

Tillering determines the plant size of sorghum (Sorghum bicolor) and an understanding of its regulation is important to match genotypes to prevalent growing conditions in target production environments. The aim of this study was to determine the physiological and environmental regulation of variability in tillering among sorghum genotypes, and to develop a framework for this regulation. * Diverse sorghum genotypes were grown in three experiments with contrasting temperature, radiation and plant density to create variation in tillering. Data on phenology, tillering, and leaf and plant size were collected. A carbohydrate supply/demand (S/D) index that incorporated environmental and genotypic parameters was developed to represent the effects of assimilate availability on tillering. Genotypic differences in tillering not explained by this index were defined as propensity to tiller (PTT) and probably represented hormonal effects. * Genotypic variation in tillering was associated with differences in leaf width, stem diameter and PTT. The S/D index captured most of the environmental effects on tillering and PTT most of the genotypic effects. * A framework that captures genetic and environmental regulation of tillering through assimilate availability and PTT was developed, and provides a basis for the development of a model that connects genetic control of tillering to its phenotypic consequences.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

Genetic programming metamodel for rotating beams

This paper investigates the use of Genetic Programming (GP) to create an approximate model for the non-linear relationship between flexural stiffness, length, mass per unit length and rotation speed associated with rotating beams and their natural frequencies. GP, a relatively new form of artificial intelligence, is derived from the Darwinian concept of evolution and genetics and it creates computer programs to solve problems by manipulating their tree structures. GP predicts the size and structural complexity of the empirical model by minimizing the mean square error at the specified points of input-output relationship dataset. This dataset is generated using a finite element model. The validity of the GP-generated model is tested by comparing the natural frequencies at training and at additional input data points. It is found that by using a non-dimensional stiffness, it is possible to get simple and accurate function approximation for the natural frequency. This function approximation model is then used to study the relationships between natural frequency and various influencing parameters for uniform and tapered beams. The relations obtained with GP model agree well with FEM results and can be used for preliminary design and structural optimization studies.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

Using IRAP markers for analysis of genetic variability in populations of resource and rare species of plants

Species specific LTR retrotransposons were first cloned in five rare relic species of drug plants located in the Perm’ region. Sequences of LTR retrotransposons were used for PCR analysis based on amplification of repeated sequences from LTR or other sites of retrotransposons (IRAP). Genetic diversity was studied in six populations of rare relic species of plants Adonis vernalis L. by means of the IRAP method; 125 polymorphic IRAP markers were analyzed. Parameters for DNA polymorphism and genetic diversity of A. vernalis populations were determined.

A Dynamic Migration Model for self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

Using IRAP markers for analysis of genetic variability in populations of resource and rare species of plants

Species specific LTR retrotransposons were first cloned in five rare relic species of drug plants located in the Perm’ region. Sequences of LTR retrotransposons were used for PCR analysis based on amplification of repeated sequences from LTR or other sites of retrotransposons (IRAP). Genetic diversity was studied in six populations of rare relic species of plants Adonis vernalis L. by means of the IRAP method; 125 polymorphic IRAP markers were analyzed. Parameters for DNA polymorphism and genetic diversity of A. vernalis populations were determined.

Identification of delamination in composite beams using spectral estimation and a genetic algorithm

An efficient strategy for identification of delamination in composite beams and connected structures is presented. A spectral finite-element model consisting of a damaged spectral element is used for model-based prediction of the damaged structural response in the frequency domain. A genetic algorithm (GA) specially tailored for damage identification is derived and is integrated with finite-element code for automation. For best application of the GA, sensitivities of various objective functions with respect to delamination parameters are studied and important conclusions are presented. Model-based simulations of increasing complexity illustrate some of the attractive features of the strategy in terms of accuracy as well as computational cost. This shows the possibility of using such strategies for the development of smart structural health monitoring softwares and systems.

A novel approach for Tuning of minimum performance robust PSS using Genetic algorithm

This paper presents a novel approach for designing a fixed gain robust power system stabilizer (PSS) with particu lar emphasis on achieving a minimum closed loop perfor mance, over a wide range of operating and system condi tion. The minimum performance requirements of the con troller has been decided apriori and obtained by using a genetic algorithm (GA) based power system stabilizer. The proposed PSS is robust to changes in the plant parameters brought about due to changes in system and operating con dition, guaranteeing a minimum performance. The efficacy of the proposed method has been tested on a multimachine system. The proposed method of tuning the PSS is an at tractive alternative to conventional fixed gain stabilizer de sign, as it retains the simplicity of the conventional PSS and still guarantees a robust acceptable performance over a wider range of operating and system condition.

Genetic Algorithm for Damage Assessment

Genetic Algorithms (GAs) are recognized as an alternative class of computational model, which mimic natural evolution to solve problems in a wide domain including machine learning, music generation, genetic synthesis etc. In the present study Genetic Algorithm has been employed to obtain damage assessment of composite structural elements. It is considered that a state of damage can be modeled as reduction in stiffness. The task is to determine the magnitude and location of damage. In a composite plate that is discretized into a set of finite elements, if a jth element is damaged, the GA based technique will predict the reduction in Ex and Ey and the location j. The fact that the natural frequency decreases with decrease in stiffness is made use of in the method. The natural frequency of any two modes of the damaged plates for the assumed damage parameters is facilitated by the use of Eigen sensitivity analysis. The Eigen value sensitivities are the derivatives of the Eigen values with respect to certain design parameters. If ωiu is the natural frequency of the ith mode of the undamaged plate and ωid is that of the damaged plate, with δωi as the difference between the two, while δωk is a similar difference in the kth mode, R is defined as the ratio of the two. For a random selection of Ex,Ey and j, a ratio Ri is obtained. A proper combination of Ex,Ey and j which makes Ri−R=0 is obtained by Genetic Algorithm.

Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters

The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for para-meters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.