Fitness Function Of Genetic Algorithm Research Articles

DWFS: a wrapper feature selection tool based on a parallel genetic algorithm.

Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features. The smaller number of features reduces the problem’s dimensionality and may result in higher classification performance. We developed DWFS, a web-based tool that allows for efficient selection of features for a variety of problems. DWFS follows the wrapper paradigm and applies a search strategy based on Genetic Algorithms (GAs). A parallel GA implementation examines and evaluates simultaneously large number of candidate collections of features. DWFS also integrates various filtering methods that may be applied as a pre-processing step in the feature selection process. Furthermore, weights and parameters in the fitness function of GA can be adjusted according to the application requirements. Experiments using heterogeneous datasets from different biomedical applications demonstrate that DWFS is fast and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods. DWFS can be accessed online at www.cbrc.kaust.edu.sa/dwfs.

Implementation of Energy Efficient Coverage Aware Routing Protocol for Wireless Sensor Network Using Genetic Algorithm

In recent years, wireless sensor network have been used in many application such as disaster reservation, agriculture, environmental observation and forecasting .Coverage preservation and energy consumption are two most important issues in wireless sensor networks. To increase the network lifetime, we propose an energy efficient coverage aware routing protocol for wireless sensor network for randomly deployed sensor nodes. Some of the routing protocol is based on energy efficiency and some are based on coverage aware. The proposed routing protocol is based on both the issues i.e. coverage and energy, in which we first find the k-mean i.e. the degree of coverage, so that we can use this in the selection of cluster heads in wireless sensor network by using Genetic Algorithm for increasing network lifetime and coverage. For cluster head selection each node evaluates its k-mean and energy by internal function which used as fitness function in genetic algorithm. The proposed algorithm “Implementation of energy efficient coverage aware routing protocol for Wireless Sensor Network” is designed for homogeneous wireless sensor network. Simulations results show that proposed algorithm increases the network lifetime by reduce the energy consumption and preserve coverage. Simulation is done with MATLAB and a comparison of algorithm with benchmark algorithms is also performed.

Foreign Fiber Image Segmentation Based on Maximum Entropy and Genetic Algorithm

In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and what’s more, the brightness and contrast of the image are all poor. Using the traditional image segmentation method, the segmentation results are very poor. By adopting the maximum entropy and genetic algorithm, the maximum entropy function was used as the fitness function of genetic algorithm. Through continuous optimization, the optimal segmentation threshold is determined. Experimental results prove that the image segmentation of this paper not only fast and accurate, but also has strong adaptability.

Crop Disease Leaf Image Segmentation Based on Genetic Algorithm and Maximum Entropy

Crop disease leaf image segmentation is a key step in crop disease recognition. In the paper, a segmentation method of crop disease leaf image is proposed to segment leaf image with non-uniform illumination based on maximum entropy and genetic algorithm (GA). The information entropy is regarded as the fitness function of GA, the maximum entropy as convergence criterion of GA. After genetic operation, the optimal threshold is obtained to segment the image of disease leaf. The experimental results of the maize disease leaf image show that the proposed method can select the threshold automatically and efficiently, and has an advantage over the other three algorithms, and also can reserve the main spot features of the original disease leaf image.

개선된 배깅 앙상블을 활용한 기업부도예측

개선된 배깅 앙상블을 활용한 기업부도예측

Application of a modified AEP method for calculation and synthesis of large-scale arrays

The standard active element pattern (AEP) method is presented to predict the radiation from large-scale arrays, in which the AEPs of large array are approximated by those of a small array. However, this method needs to compute and store each AEP of the small array. To avoid this fussy process, a novel AEP method is derived, which simply formulates the radiation field of the large array with that of two small arrays. Furthermore, using the AEP method to evaluate the fitness function of genetic algorithm, a low sidelobe level linear array of 16 aperture-coupled microstrip antennas is designed by optimisation of the element excitations. Numerical results show that the modified AEP method has the same accuracy as the Ansoft HFSS simulation while saving 71% computational cost compared to the standard AEP method.

Hybrid GA for material routing optimization in supply chain

In designing a supply chain (SC) system, the problem arises when a company has unsatisfactory inventory control policy and material routing between supplier–producer and distributor in SC considering specified cost and demand. The integration of decisions of different functions into a single optimization model is the base of this research. The aim of this paper is to study and compare the existing models of supply, production and distribution in SC and propose a model which integrates mentioned criteria in supply chain management (SCM). Furthermore, it proposes a new method for calculation of fitness function in genetic algorithm (GA) process. The successful designing of this model has led us to explore the use of heuristic methods such as GA to quantify the flow of SC, information and material flow. At first, fuzzy analytic hierarchy process (FAHP) is adapted to evaluate objective function weights in SC. Then final weights of objective function are determined by the technique for order of preference by similarity to ideal solution (TOPSIS). This research also simulated the real company SC operations, and determines the most effective strategic and operational policies for an effective SC system. The result obtained from the model shows that it is robust. This model can also be applied to other industrial environments with slight modifications.

Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining

Extraction of knowledge in agricultural data is a challenging task, from discovering patterns and relationships and interpretation. In order to obtain potentially interesting patterns and relationships from this data, it is therefore essential that a methodology be developed and take advantage of the sets of existing methods and tools available for data mining and knowledge discovery in databases. Data mining is relatively a new approach in the field of agriculture. Accurate information in characterizing crops depends on climatic, geographical, biological and other factors. These are very important inputs to generate characterization and prediction models in data mining. In this study, an efficient data mining methodology based on PCA-GA is explored, presented and implemented to characterize agricultural crops. The method draws improvements to classification problems by using Principal Components Analysis (PCA) as a pre processing method and a modified Genetic Algorithm (GA) as the function optimizer. The fitness function in GA is modified accordingly using efficient distance measures. The approach is to asses, the PCA-GA hybrid data mining method, using various agricultural field data sets, generate data mining classification models and establish meaningful relationships. The experimental results show improved classification rates and generated characterization models for agricultural crops. The domain model outcome may have benefits, to agricultural researchers and farmers. These generated classification models can also be utilized and readily incorporated into a decision support system.

SNPs selection using support vector regression and genetic algorithms in GWAS.

IntroductionThis paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence.ResultsThe suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS.ConclusionsThe method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels.

Research on Optimal Control Model for Ball Mill Based on Genetic Algorithm

As the key equipment in coal-fired power plants, the optimal control problem of ball mill is an important factor affecting the efficient operation of the mill. In this paper, the output model, energy consumption model and unit consumption model of coal pulverizing system are built on the basis of analysis of ball mill running condition. The unit consumption model is used as the genetic algorithm fitness function. The outlet temperature, practical ventilation and coal load of ball mill are chosen as the optimization variables. The algorithm model is built to solve the multi-objective problem of ball mill.

Optimal Sizing Supercapacitor-Battery Hybrid Energy Storage System in Solar Application using The Genetic Algorithms

This paper deals with an approach to optimally size a supercapacitor-battery hybrid energy storage system for solar applications using the Genetic Algorithm (GA). GA simulation shows that the cost of the proposed supercapacitor-battery renewable energy system is lower than the cost of the conventional renewable energy system, which contains only battery system as energy storage devices. This approach suggests the optimal number of components used in the renewable energy system and ensures that the 20-year round total system cost is optimized subject to the constraint that the load energy requirements are completely covered (zero load rejection) or capacity shortage of 1% and 2%. The implemented GA fitness function optimized the 20-year round total system cost which equals to the sum of the respective component capital (initial), operational and maintenance cost. The concept of the proposed supercapacitor-battery hybrid energy storage system exploits the strengths and compensates for the weakness of each storage device. This means that the batteries which known as a high energy density is sized at the average power for delivering the average power, whereas, supercapacitors is designed to cater the peak power. This is feasible because the supercapacitor has lower resistance which is able to shield the battery from at least a portion of the current pulses and thus extend the battery lifetime. This not only aids the reduction of the cost of replacement batteries throughout the project lifetime but it is also said to be a more environmental friendly system. The main contribution of this approach is to optimize the cost of the Supercapacitor-battery hybrid energy storage system in renewable energy system which cannot be solved in most of the commercial simulation tools, such as HOMER and HYBRIDS due to the absence of the supercapacitor components in these commercial software.

최적 퍼지PID제어기를 이용한 자기부상시스템의 부상제어기 설계

본 논문에서는 최적화 기법과 퍼지 PID제어기를 이용한 자기부상시스템의 부상제어기를 제안한다. 흡인식 자기부상시스템은 일반적으로 비선형 특성을 포함하는 불안정한 시스템이기 때문에 일반적인 선형 제어기를 적용할 경우 우수한 성능을 얻기에는 어려운 점이 있다. 본 논문에서 제안된 제어기는 고정파라미터를 갖는 퍼지 PID제어기를 이용하였으며, 퍼지 PID 제어기 파라미터들은 유전자 알고리즘을 이용하여 최적화를 수행하였으며 유전알고리즘의 목적함수로는 일반적으로 사용되는 성능지수 함수를 사용하였다. 제안된 퍼지 PID 제어기를 적용한 자기부상시스템의 성능을 평가하기 위하여 Matlab을 이용하여 시뮬레이션을 수행하였으며, 고전적 PID제어기와 성능을 비교 분석하였다. 제안된 제어기를 적용한 자기부상시스템의 성능이 고전 PID제어기에 비하여 더 우수한 성능을 보임을 시뮬레이션을 통하여 검증하였다. This paper proposes a optimum design method for the Fuzzy PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV). Since an attraction type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the methods designed by conventional controllers. In the paper, the Fuzzy PID controller with fixed parameters are applied and then the optimum parameters of fuzzy PID controller are selected by genetic algorithm. For the fitness function of genetic algorithm, the performance index of PID controller is used. To verify the performance of the proposed method, we used Matlab/simulink model of Maglev and compared the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.

Optimum design parameters and operating condition for maximum power of a direct methanol fuel cell using analytical model and genetic algorithm

It is well known that anode and cathode pressures, cell temperature and channel geometry are the effective parameters in the performance of DMFC (direct methanol fuel cell). In the present paper, the GA (genetic algorithm) as one of the most powerful optimization tools is applied to determine the optimal values for these parameters which result in maximum power density of a DMFC. The predominant part of the genetic algorithm is the fitness function. For the fitness function calculation, calculation of more than one thousand cases is necessary. Unfortunately, large numbers of experiments are needed, which is very time-consuming and costly. To overcome this challenge, a quasi two dimensional, isothermal model is used to obtain the power of DMFC as the fitness function of GA. For validation of this model, the results of the model are compared with experimental results and literature and shown to be in good agreement with them.

A Genetic Algorithm for Locating the Multiscale Critical Slip Surface in Jointed Rock Mass Slopes

The joints have great influence on the strength of jointed rock mass and lead to the multiscale, nonhomogeneous, and anisotropic characteristics. In order to consider these effects, a new model based on a genetic algorithm is proposed for locating the critical slip surface (CSS) in jointed rock mass slope (JRMS) from its stress field. A finite element method (FEM) was employed to analyze the stress field. A method of calculating the mechanical persistence ratio (MPR) was used. The calculated multiscale and anisotropic characteristics of the MPR were used in the fitness function of genetic algorithm (GA) to calculate the factor of safety. The GA was used to solve optimization problems of JRMS stability. Some numerical examples were given. The results show that the multiscale and anisotropic characteristics of the MPR played an important role in locating the CSS in JRMS. The proposed model calculated the CSS and the factor of safety of the slope with satisfactory precision.

An Improved Data Mining Mechanism Based on PCA-GA for Agricultural Crops Characterization

In this study, a data mining method based on PCA-GA is presented to characterize agricultural crops. Specifically it draws improvements to classification problems by using Principal Components Analysis (PCA) as a preprocessing method and a modified Genetic Algorithm (GA) as the function optimizer. The GA performs the optimization process, selecting the most suited set of features that determines the class of a crop it belongs to. The fitness function in GA is studied and modified accordingly using efficient distance measures. The soybean dataset is used in the experiment and results are compared with several classifiers. The experimental results show improved classification rates. This lessens the time consumed of agricultural researchers in characterizing agricultural crops.

Integration of genetic algorithm, analytic hierarchy process and computer simulation for optimisation of operator allocation in manufacturing systems with weighted variables

This paper presents an integrated analytic hierarchy process (AHP) genetic algorithm (GA) and computer simulation (CS) for the optimisation of operator allocation in cellular manufacturing systems (CMS) with weighted variables. This is a challenging issue in manufacturing systems in general and in CMS in particular. A computer simulation model which considers various operators layout is developed. GA is utilised for optimisations of results of various operators layout and AHP is utilised to define the weight of variables of fitness function for GA. Previous studies only utilise multivariate analysis methods, fuzzy C-means and simulation, whereas this study uses an integrated GA, AHP and simulation for CMS with weighted variables. Also, previous CMS studies consider only one type of product whereas this study considers multi product CMS modelling through simulation. Moreover, more robust CMS assessment indicators are used in the proposed model. A practical case study illustrates the effectiveness and superiority of the proposed methodology.

허리 관절을 갖는 4족 로봇의 GA 기반 경사면 보행방법

본 논문에서는 허리 관절을 갖는 4족 로봇의 효율적인 경사면 보행을 위해 유전 알고리듬(Genetic Algorithm: GA)을 이용한 경사면 보행 방법을 제안한다. 제안한 방법에서는 먼저, 허리 관절을 갖는 4족 로봇의 기구학적 모델을 유도하며, GA를 수행하기 위한 유전자 및 적합도 함수를 설정한다. 또한, 경사면에서 최적의 에너지 안정여유도(Energy Stability Margin: ESM)를 갖는 4족 로봇의 자세와 도달 영역 내의 발끝 착지 지점을 GA를 이용하여 자동으로 탐색하여 보행한다. 마지막으로, 4족 보행 로봇의 모의 실험을 통해 기존 방법과 비교함으로써 본 논문에서 제안한 방법의 효용성을 검증한다. In this paper, we propose a genetic algorithm(GA) based locomotion method of a quadruped robot with waist joint, which makes a quadruped robot walk on the slop efficiently. In the proposed method, we first derive the kinematic model of a quadruped robot with waist joint and then set the gene and the fitness function for GA. In addition, we determine the best attitude for a quadruped robot and the landing point of a foot in the walk space, which has the optimal energy stability margin(ESM). Finally, we verify the effectiveness of the proposed method by comparing with the performance of the previous method through the computer simulations.

Evolutionary ELM wrapper feature selection for Alzheimer's disease CAD on anatomical brain MRI

This paper proposes an evolutionary wrapper feature selection using Extreme Learning Machines (ELM) as the base classifier training algorithm, comprising a Genetic Algorithm (GA) exploring the space of feature combinations. GA fitness function is the mean accuracy of a cross-validation evaluation of each individual feature selection. The marginal distribution of the classification accuracy corresponding to a feature is used to measure feature saliency. The raw features are extracted as a voxel selection from anatomical brain magnetic resonance imaging (MRI). Voxel selection is provided by Voxel Based Morphometry (VBM) which finds statistically significant clusters of voxels that have differences across MRI volumes on a paired dataset of Alzheimer's Disease (AD) and healthy controls.

Study of Multi-Project Conflicts by IGA Based on the Improvement of Fitness Function

In the multi-project resource conflicts exist in the application of standard genetic algorithm fitness function exist "premature" problem, Genetic algorithm can not find the convergence of these issue. Based on the above issues ,an improved genetic algorithm (IGA) are appropriate, From the fitness function, mutation and selection methods to improve two aspects are described, the Improved genetic algorithm for simple genetic algorithm has the advantage of generations of each evolution, offspring parent always retains the best individual to the "high-fitness model for the ancestors of the family orientation" search out better samples, and verified through experiments the effectiveness of the algorithm

Optimization of Stacking Sequence of Composite Laminates for Optimizing Buckling Load by Neural Network and Genetic Algorithm

Composite beams, plates and shells are widely used in the aerospace industry because of their advantages over the commonly used isotropic structures especially when it comes to weight savings. Buckling analyses of composite structural components must be performed in order to ensure, for instance, that a composite panel designed to be part of a control surface does not buckle thereby compromising its aerodynamic shape. Optimization of composite structures has been performed in this paper using Genetic algorithm. Genetic algorithm (GA) approaches are successfully implemented for the TSP. The buckling load of composite plate, which is obtained by the Artificial Neural Networks, was used as the fitness function in the GA to find its optimized value by arranging the ply stacking sequence.