Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.

Whole genome SNPs among 8 chicken breeds enable identification of genetic signatures that underlie breed features

Many different chicken breeds are found around the world,their features vary among them,and they are valuable resources.Currently,there is a huge lack of knowledge of the genetic determinants responsible for phenotypic and biochemical properties of these breeds of chickens.Understanding the underlying genetic mechanisms that explain across-breed variation can help breeders develop improved chicken breeds.The whole-genomes of 140 chickens from 7 Shandong native breeds and 20 introduced recessive white chickens from China were re-sequenced.Comparative population genomics based on autosomal single nucleotide polymorphisms(SNPs)revealed geographically based clusters among the chickens.Through genome-wide scans for selective sweeps,we identified thyroid stimulating hormone receptor(TSHR,reproductive traits,circadian rhythm),erythrocyte membrane protein band 4.1 like 1(EPB41L1,body size),and alkylglycerol monooxygenase(AGMO,aggressive behavior),as major candidate breed-specific determining genes in chickens.In addition,we used a machine learning classification model to predict chicken breeds based on the SNPs significantly associated with recourse characteristics,and the prediction accuracy was 92%,which can effectively achieve the breed identification of Laiwu Black chickens.We provide the first comprehensive genomic data of the Shandong indigenous chickens.Our analyses revealed phylogeographic patterns among the Shandong indigenous chickens and candidate genes that potentially contribute to breed-specific traits of the chickens.In addition,we developed a machine learning-based prediction model using SNP data to identify chicken breeds.The genetic basis of indigenous chicken breeds revealed in this study is useful to better understand the mechanisms underlying the resource characteristics of chicken.

Prediction of Pressure Drop of Slurry Flow in Pipeline by Hybrid Support Vector Regression and Genetic Algorithm Model

这份报纸描述柔韧的支持向量回归(SVR ) 方法论，它能为重要过程工程问题提供优异性能。方法为 SVR 元参数的有效调节合并混合支持向量回归和基因算法技术(SVR-GA ) 。算法被申请了稳固的液体的压力落下的预言泥浆流动。有在文学的选择关联的比较证明发达 SVR 关联显著地在大量操作条件，物理性质，和管子直径上改进了压力落下的预言。

Expression Vector Construction and Genetic Transformation of <i>Rosa rugosa β</i>-l,3-Glucanase Gene (<i>RrGlu</i>)

In order to lay a foundation for researching the function of Rosa rugose (R. rugosa) RrGlu gene, the RrGlu gene was amplified from the styles of R. rugosa “Tanghong”, a gene expression vector named PBI121-RrGlu was constructed and the vector was introduced into tobacco with the agrobacterium-mediated method. PCR results showed that the RrGlu gene was integrated into the tobacco genome.

Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting

Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.

Support Vector Machines Networks to Hybrid Neuro-Genetic SVMs in Portfolio Selection

Corporate net value is efficiently described on its stock price, offering investors a chance to include a potentially surplus value to the net worth of the overall investment portfolio. Financial analysis of corporations extracted from the accounting statements is constantly demanded to support decisions making of portfolio managers. Econometrics and Artificial Intelligence methods aim to extract hidden information from complex accounting and financial data. Support Vector Machines hybrids optimized in their components by Genetic Algorithms provide effective results in corporate financial analysis.

Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine

Parkinson’s disease (PD) is the most common disease of motor system degeneration that occurs when the dopamine-producing cells are damaged in substantia nigra. To detect PD, various signals have been investigated, including EEG, gait and speech. Since approximately 90 percent of the people with PD suffer from speech disorders, speech analysis is considered as the most common technique for this aim. This paper proposes a new algorithm for diagnosing of Parkinson’s disease based on voice analysis. In the first step, genetic algorithm (GA) is undertaken for selecting optimized features from all extracted features. Afterwards a network based on support vector machine (SVM) is used for classification between healthy and people with Parkinson. The dataset of this research is composed of a range of biomedical voice signals from 31 people, 23 with Parkinson’s disease and 8 healthy people. The subjects were asked to pronounce letter “A” for 3 seconds. 22 linear and non-linear features were extracted from the signals that 14 features were based on F0 (fundamental frequency or pitch), jitter, shimmer and noise to harmonics ratio, which are main factors in voice signal. Because changing in these factors is noticeable for the people with PD, optimized features were selected among them. Of the various numbers of optimized features, the data classification was investigated. Results show that the classification accuracy percent of 94.50 per 4 optimized features, the accuracy percent of 93.66 per 7 optimized features and the accuracy percent of 94.22 per 9 optimized features, could be achieved. It can be observed that the best classification accuracy may be achieved using Fhi (Hz), Fho (Hz), jitter (RAP) and shimmer (APQ5).

Genetic algorithm tuned PI controller on PMSM simplified vector control

A simple control structure in servo system is occasionally needed for simple industrial application which precise and high control performance is not exessively important so that the cost production can be reduced efficiently.Simplified vector control,which has simple control structure,is utilized as the permanent magnet synchronous motor control algorithm and genetic algorithm is used to tune three PI controllers used in simplified vector control.The control performance is obtained from simulation and investigated to verify the feasibility of the algorithm to be applied in the real application.Simulation results show that the speed and torque responses of the system in both continuous time and discrete time can achieve good performances.Furthermore,simplified vector control combined with genetic algorithm has a similar performance with conventional field oriented control algorithm and possible to be realized into the real simple application in the future.

Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine

In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.

Assessing supply chain performance using genetic algorithm and support vector machine

The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of supply chain to obtain the core influencing factors. Then the support vector machine is used to extract the core influencing factors to predict the level of supply chain performance. In the process of SVM classification, the genetic algorithm is used to optimize the parameters of the SVM algorithm to obtain the best parameter model, and then the supply chain performance evaluation level is predicted. Finally, an example is used to predict this model, and compared with the result of using only rough set-support vector machine to predict. The results show that the method of rough set-genetic support vector machine can predict the level of supply chain performance more accurately and the prediction result is more realistic, which is a scientific and feasible method.

Support Vector Machine Ensemble Based on Genetic Algorithm

Support vector machines (SVMs) have been introduced as effective methods for solving classification problems. However, due to some limitations in practical applications, their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE. Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs, bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.

Applications and developments of gene therapy drug delivery systems for genetic diseases

Genetic diseases seriously threaten human health and have always been one of the refractory conditions facing humanity.Currently,gene therapy drugs such as siRNA,shRNA,antisense oligonucleotide,CRISPR/Cas9 system,plasmid DNA and miRNA have shown great potential in biomedical applications.To avoid the degradation of gene therapy drugs in the body and effectively deliver them to target tissues,cells and organelles,the development of excellent drug delivery vehicles is of utmost importance.Viral vectors are the most widely used delivery vehicles for gene therapy in vivo and in vitro due to their high transfection efficiency and stable transgene expression.With the development of nanotechnology,novel nanocarriers are gradually replacing viral vectors,emerging superior performance.This review mainly illuminates the current widely used gene therapy drugs,summarizes the viral vectors and non-viral vectors that deliver gene therapy drugs,and sums up the application of gene therapy to treat genetic diseases.Additionally,the challenges and opportunities of the field are discussed from the perspective of developing an effective nano-delivery system.

Eradication of malaria through genetic engineering:the current situation

Malaria is an intra-cellular parasitic protozoon responsible for millions of deaths annually.Host and parasite genetic factors are crucial in affecting susceptibility to malaria and progression of the disease.Recent increased deployment of vector controls and new artemisinin combination therapies have dramatically reduced the mortality and morbidity of malaria worldwide.However, the gradual emergence of parasite and mosquito resistance has raised alarm regarding the effectiveness of current artemisinin-based therapies.In this review,mechanisms of anti-malarial drug resistance in the Plasmodium parasite and new genetically engineered tools of research priorities are discussed.The complexity of the parasite lifecycle demands novel interventions to achieve global eradication.However,turning laboratory discovered transgenic interventions into functional products entails multiple experimental phases in addition to ethical and safety hurdles.Uncertainty over the regulatory status and public acceptance further discourage the implementation of genetically modified organisms.

Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold

Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type,scenarios and level of blurriness.In this paper,we propose an effective method for blur detection and segmentation based on transfer learning concept.The proposed method consists of two separate steps.In the first step,genetic programming(GP)model is developed that quantify the amount of blur for each pixel in the image.The GP model method uses the multiresolution features of the image and it provides an improved blur map.In the second phase,the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold.A model based on support vector machine(SVM)is developed to compute adaptive threshold for the input blur map.The performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art methods.The comparative analysis reveals that the proposed method performs better against the state-of-the-art techniques.

Position Vectors Based Efcient Indoor Positioning System

With the advent and advancements in the wireless technologies,Wi-Fi ngerprinting-based Indoor Positioning System(IPS)has become one of the most promising solutions for localization in indoor environments.Unlike the outdoor environment,the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efcient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things(IoTs)and green computing.In this paper,we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors.Initially,in the database development phase,Motley Kennan propagation model is used with Hough transformation to classify,detect,and assign different attenuation factors related to the types of walls.Furthermore,important parameters for system accuracy,such as,placement and geometry of Access Points(APs)in the coverage area are also considered.New algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm(GA)coupled with Enhanced Dilution of Precision(EDOP).Moreover,classication algorithm based on k-Nearest Neighbors(k-NN)is used to nd the position of a stationary or mobile user inside the given coverage area.For k-NN to provide low localization error and reduced space dimensionality,three APs are required to be selected optimally.In this paper,we have suggested an idea to select APs based on Position Vectors(PV)as an input to the localization algorithm.Deducing from our comprehensive investigations,it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with signicant improvements.

Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning

A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algorithm and multiple instance learning(MIL).The band selection method was proposed from subspace decomposition,which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities,as well as mutation individuals.Then MIL was combined with image segmentation,clustering and support vector machine algorithms to classify hyperspectral image.The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.

Generating Epsilon-Efficient Solutions in Multiobjective Optimization by Genetic Algorithm

We develop a new evolutionary method of generating epsilon-efficient solutions of a continuous multiobjective programming problem. This is achieved by discretizing the problem and then using a genetic algorithm with some derived probabilistic stopping criteria to obtain all minimal solutions for the discretized problem. We prove that these minimal solutions are the epsilon-optimal solutions to the original problem. We also present some computational examples illustrating the efficiency of our method.

Comparative Study of Variable Selection Using Genetic Algorithm with Various Types of Chromosomes

In this study,different methods of variable selection using the multilinear step-wise regression（MLR） and support vector regression（SVR） have been compared when the performance of genetic algorithms（GAs） using various types of chromosomes is used.The first method is a GA with binary chromosome（GA-BC） and the other is a GA with a fixed-length character chromosome（GA-FCC）.The overall prediction accuracy for the training set by means of 7-fold cross-validation was tested.All the regression models were evaluated by the test set.The poor prediction for the test set illustrates that the forward stepwise regression（FSR） model is easier to overfit for the training set.The results using SVR methods showed that the over-fitting could be overcome.Further,the over-fitting would be easier for the GA-BC-SVR method because too many variables fleetly induced into the model.The final optimal model was obtained with good predictive ability（R2 = 0.885,S = 0.469,Rcv2 = 0.700,Scv = 0.757,Rex2 = 0.692,Sex = 0.675） using GA-FCC-SVR method.Our investigation indicates the variable selection method using GA-FCC is the most appropriate for MLR and SVR methods.

Comparisons of VAR Model and Models Created by Genetic Programming in Consumer Price Index Prediction in Vietnam

In this paper, we present an application of Genetic Programming (GP) to Vietnamese CPI in?ation one-step prediction problem. This is a new approach in building a good forecasting model, and then applying inflation forecasts in Vietnam in current stage. The study introduces the within-sample and the out-of-samples one-step-ahead forecast errors which have positive correlation and approximate to a linear function with positive slope in prediction models by GP. We also build Vector Autoregression (VAR) model to forecast CPI in quaterly data and compare with the models created by GP. The experimental results show that the Genetic Programming can produce the prediction models having better accuracy than Vector Autoregression models. We have no relavant variables (m2, ex) of monthly data in the VAR model, so no prediction results exist to compare with models created by GP and we just forecast CPI basing on models of GP with previous data of CPI.

Extrapolation for Aeroengine Gas Path Faults with SVM Bases on Genetic Algorithm

Mining aeroengine operational data and developing fault diagnosis models for aeroengines are to avoid running aeroengines under undesired conditions.Because of the complexity of working environment and faults of aeroengines,it is unavoidable that the monitored parameters vary widely and possess larger noise levels.This paper reports the extrapolation of a diagnosis model for 20 gas path faults of a double-spool turbofan civil aeroengine.By applying support vector machine(SVM)algorithm together with genetic algorithm(GA),the fault diagnosis model is obtained from the training set that was based on the deviations of the monitored parameters superimposed with the noise level of 10%.The SVM model(C=24.7034;γ=179.835)was extrapolated for the samples whose noise levels were larger than 10%.The accuracies of extrapolation for samples with the noise levels of 20%and 30%are 97%and 94%,respectively.Compared with the models reported on the same faults,the extrapolation results of the GASVM model are accurate.