in a preliminary study, descriptive cross sectional, knowledge of 550 general practitioners, pediatrics and gynecology residents were evaluated in relation to medical genetics. a questionnaire was developed by researchers, and based on bloom's taxonomy; the lowest learning level (i.e. knowledge) was developed as multiple choices. nonrandom sampling (convenience sampling) was conducted that was ...

The availability of the human genome sequence has revolutionized human genetics research. By studying the differences in the genomes between sick and healthy individuals one can associate particular differences with the disease. Such association is the first step towards understanding disease cause, diagnostics and eventually therapy. In this thesis we study several problems related to the most...

Genetic algorithms are random heuristic search algorithms which mimic biological evolution and molecular genetics in simplified form. These algorithms can be theoretically described by an infinite population model with the help of a deterministic dynamical system by which the stochastic population trajectory is characterized using a deterministic heuristic function and its fixed points. For pra...

For the simplicity of implementation and saving of operation time, the "xed-order optimal deconvolution "lter design is appealing for engineers in signal processing from practical design perspective. In this study, a design method based on genetic algorithms is proposed to simultaneously treat with H 2 and H = optimal signal reconstruction design problem with prescribed "lter order. Genetic alg...

Genetic Algorithms (GAs) are adaptive heuristic search algorithms based on the evolutionary ideas of natural selection and natural genetics. The coding and manipulation of search data is based upon the operation of genetic DNA and the selection process is derived from Darwin’s survival of the fittest’. Evolutionary computing was introduced in the 1960s by I. Rechenberg in his work “Evolution st...

Interpretability and accuracy are two important features of fuzzy systems which are conflicting in their nature. One can be improved at the cost of the other and this situation is identified as “Interpretability-Accuracy Trade-Off”. To deal with this trade-off Multi-Objective Evolutionary Algorithms (MOEA) are frequently applied in the design of fuzzy systems. Several novel MOEA have been propo...

A parallel genetic algorithm is presented to solve the well-known ca-pacitated lot-sizing problem. The approach is implemented on a massively parallel single instruction multiple data architecture with 16384 4-bit processors. Based on a random keys representation a schedule is backward oriented obtained which enables us to apply a very simple capacity check. Genetic algorithms are a general pur...

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from inpu...

For traffic prediction forecasting in WIMAX, generally the genetic algorithm were used ,but due to certain limitation of genetic algorithms, problems was created in the traffic prediction .So there is a need to proposed a new approach that is better than Genetic algorithm .this approach need to update the limitation of the Genetics algorithm. There is a need to enhance the prediction capability...

This is a survey of the field of Genetics-based Machine Learning (GBML): the application of evolutionary algorithms to machine learning. We assume readers are familiar with evolutionary algorithms and their application to optimisation problems, but not necessarily with machine learning. We briefly outline the scope of machine learning, introduce the more specific area of supervised learning, co...