Study of Genetic Parameters in F5 Families of Rice (Oryza sativa L.)

One hundred and fourteen F5 families of rice belonging to six crosses along with seven parents were evaluated during kharif, 2015 at Andhra Pradesh Rice Research Institute and Regional Agricultural Research Station, Maruteru to study variability, heritability, genetic advance as per cent of mean and nature and direction of association among themselves and with grain yield. Data was recorded on ten characters which showed significant differences among themselves. High PCV and GCV were observed for grain yield per plant and test weight. High heritability coupled with high genetic advance as per cent of mean was observed for number of grains per panicle, grain yield per plant and test weight indicating the presence of additive gene action in governing the inheritance of these traits. Hence, direct phenotypic selection is useful with respect to these traits

Determination of Ion Exchange Parameters by a Genetic Algorithm

Modeling the process of ion exchange in glass requires accurate knowledge of the self-diffusion coefficients of the incoming and outgoing ions. Furthermore, correlating the concentration profile of the incoming ions to a change in refractive index requires knowledge of the correlation coefficient. We present a method by which these three parameters can be quickly determined experimentally, using a genetic algorithm. Comparison with published data is presented

Genetic Parameters Estimation on Functional Dryness Traits of Crossed Black Paddy Rice "Baas Selem Cultivar X Situ Patenggang” Variety

The aims of this study were to elucidate heritability and the role of drought traits genes of black paddy rice for determination base of the selection method to obtain drought tolerant and high yield potential of black paddy rice. The study was conducted through two experiments during February-November 2013. The first experiment was the establishment of populations from crosses carried out in the hybridization room. The second trial was evaluation of the genetic diversity of drought properties held in the greenhouse of the Faculty of Agriculture, University of Mataram. Planting was carried out in pots without experiment design. Population of P1 (parental-Situ Patenggang), P2 (parental-Baas Selem) were 50 plants of each; population of F1, F1BC.1.2, and F1BC.1.1 were 25 plants of each, and 250 plants of F2, as well as control of drought susceptible variety (IR20) was 10 plants. To determine the heritability and the role of genes controlling drought traits were used index of bud dry and cure of IRRI standard. The results showed that crossing of black paddy rice "Baas Selem x Situ Patenggang” had relatively moderate heritability in broad sense and low heritability in narrow sense. In the crossed F1 population was found that gene action of drought trait was not perfectly dominan

The estimation of genetic parameters for growth curve traits in Raeini Cashmere goat described by Gompertz model

The objectives of this study were to describe growth curve of Raeini Cashmere goat applying the Gompertz growth model and genetic evaluation of growth curve-related traits including model parameters of A, B and K, inflection age (IA) and inflection weight (IW) under animal model. The data used in this study, collected in Raeini Cashmere goat breeding station from 1997 to 2009 and were included 12,831 body weights records measured at birth, weaning, 6-months of age, 9-month of age and yearling of age. The Pearson’s correlation coefficient between observed and predicted body weights was 0.98, which means that Gompertz model adequately described the growth curve in Raeini Cashmere goat. The estimated value for growth curve parameters of A, B and K were 17.97, 1.97 and 0.017, respectively. The weight and age at point of inflection were 6.63 kg and 52.94 days, respectively. Direct heritability estimates for A, B, K, IA and IW were low values of 0.14, 0.10, 0.03, 0.14 and 0.14, respectively. Low estimated values for direct heritability of the studied growth curve traits in Raeini Cashmere goat indicated that direct selection for these traits may not be useful in terms of achieving genetic change. Direct genetic correlations ranged from −0.76 (K-IW) to 0.98 (A-IW). Phenotypic correlation estimates were generally lower than the direct genetic ones and ranged from −0.30 (K-IW) to 0.69 (A-B and B-IA). IA and IW had high positive phenotypic (0.86) and genetic (0.99) correlations, implying IA and IW were highly correlated in terms of phenotypic and genetic effects. The studied growth curve parameters of Raeini Cashmere goat have shown low levels of additive genetic variation.info:eu-repo/semantics/publishedVersio

Genetic Algorithm for quick finding of diatomic molecule potential parameters

Application of Genetic Algorithm (GA) for determination of parameters of an analytical representation of diatomic molecule potential is presented. GA can be used for finding potential characteristics of an electronic energy state which can be described by analytical function. GA was tested on two artificially generated datasets which base on potentials with known characteristics and two LIF excitation spectra recorded using transitions in CdKr and CdAr molecules. Tests on generated datasets showed that GA can properly reproduce parameters of the potentials. Tests on experimental spectra indicated that changing the potential model from Morse, which is frequently used as a starting potential in IPA, to expanded Morse oscillator (EMO) leads to noticeable improvement of agreement between simulated and experimental data

On the Implementation and Use of a Genetic Algorithm with Genetic Acquisitions

A genetic algorithm is convergent when genetic mutations occur on the objective function gradient direction. These genetic mutations are called genetic acquisitions (Mateescu, 2005). We improved the algorithm and its implementation by using the characteristics of parents in order to generate new individuals. Finally, we applied the genetic algorithm in order to find the parameters of a Cobb-Douglas function.evolutionary algorithms, optimization