Genetic Algorithm (GA) Research Papers (original) (raw)

Lossy image compression has been gaining importance in recent years due to the enormous increase in the volume of image data employed for Internet and other applications. In a lossy compression, it is essential to ensure that the compression process does not affect the quality of the image adversely. The performance of
a lossy compression algorithm is evaluated based on two conflicting parameters, namely, compression ratio and image quality which is usually measured by PSNR values. In this paper, a new lossy compression method denoted as PE-VQ method is proposed which employs prediction error and vector quantization (VQ) concepts. An
optimum codebook is generated by using a combination of two algorithms, namely, artificial bee colony and genetic algorithms. The performance of the proposed PE-VQ method is evaluated in terms of compression ratio (CR) and PSNR values using three different types of databases, namely, CLEF med 2009, Corel 1 k and standard images (Lena, Barbara etc.). Experiments are conducted for different codebook sizes and for different CR values. The results show that for a given CR, the proposed PE-VQ technique yields higher PSNR value compared to the existing algorithms. It is
also shown that higher PSNR values can be obtained by applying VQ on prediction errors rather than on the original image pixels.

In wireless sensor network nodes position
estimation in space is known as localization. Node
localization in wireless sensor network is important for
many applications and to find the position with Received
Signal Strength Indicator requires a number of anchor
nodes. However the estimation of distance from signal
strength decay in not very accurate especially in time
varying environmental conditions and the estimation of
exact direction required highly directive antenna but, may
still affected by multipath fading. A Genetic Algorithm
for wireless sensor network localization is proposed in
this paper to solve the issue that the positioning accuracy
is low with minimum anchor nodes. Hence in this paper
we are presenting a Genetic algorithm for optimization
approach which tries to find the optimal location by
satisfying both the criteria with minimal error. The
simulation results also shows effectively outperform both
the techniques

- by IJSRP Journal
- •
- Genetic Algorithms, Wireless Sensor Networks, Localization, Genetic Algorithm

To achieve the pre-set welding size, this paper presents the optimization of the constrained overlap laser welding input parameters for AISI 416 and AISI 440FSe stainless, thickness 0.5 mm. In this study, the proposed optimization algorithm is the Genetic Algorithm (GA). After training 10 times for 30 NP (population size), each training repeated 200 times, the results achieved as expected. The error is compared with the result of the affirmation experiment not exceeding 5%.

Se presenta en este artículo el desarrollo de un algoritmo genético AG para la sintonización de una estrategia de control predictiva conocida como control matricial dinámico (DMC) aplicado a un prototipo de planta de presión, el algoritmo genético permitirá encontrar los mejores parámetros de acuerdo a la minimización de índices de desempeño. Se muestra además la superioridad del DMC frente a técnicas
convencionales como el control PI por modelo interno (IMC), las ventajas son determinadas a partir de índices de desempeño obtenidos con la implementación de ambos controladores sobre el sistema, se exponen las ventajas de trabajar con las técnicas evolutivas y entre estas los AG para la sintonización de
sistemas de control que carecen de un estudio profundo de la influencia de los parámetros de sintonización sobre sus respuestas.

- by Revista Politécnica (Journal) and +1
- •
- Genetic Algorithms, Genetic Algorithm, Algoritmos Geneticos, Algoritmos Genéticos

For analysis & optimization purposes, it is necessary to represent an airfoil with fewer
parameters. In this paper, the two-dimensional surface of an airfoil has been represented by 5th order
Bézier curve. So, each of the upper and lower surface of the airfoil can be represented by (5+1) = 6 control
points only. For optimization purposes, control points of the cubic B-spline are used for modeling the airfoil
as analyses are done with Qblade. Here, the design space has been defined as the 25% above and 25%
below the y coordinates of the control points. Within this design space, two optimized shapes are obtained
by using Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) tool of Matlab respectively.
Each shape has a higher coefficient of lift-to-drag ratio (Cl/Cd) than that of the original airfoil for a range
of angle of attack (AoA). So, these two shapes can definitely be used in various aerodynamic applications
like in wind turbine and in aircraft wings to get better lift and reduced amount of drag force.

- by Usha Desai
- •
- Machine Learning, Data Mining, Fuzzy Logic, Feature Extraction

The virtual world overflowing with the digital items which make the searching, choosing and shopping hard tasks for users. The recommender system is a smart filtering tool for generate a list of potential favorite items for the user to reduce the time needed by user to
choose among a huge number of choices in websites and facilitate the process.
In that context, this thesis presents a novel technique that combines the ideas of item-based semantic similarity, n-criteria and multi-filtering criteria with the genetic-based recommender system. The genetic algorithm is utilized in order to predict the best list of items to the active user. Consequently, each individual in the population represents a candidate recommendation list. Each list subjects to three tests to measure the quality of it.
The proposed system alleviates the effect of the sparsity and cold start problems and makes the recommender system capable of generating recommendation without the need of using a similarity metric or requires any additional information provided by the hybrid system. Furthermore and due to the fact that there are many environments facing the information overload problem, the author presents a new classification of the recommender system based on the environment that is applied in.
The proposed system is evaluated against the state-of-the-art genetic-based recommender system and the traditional techniques that used in collaborative filtering recommender system. The results obtained show that the proposed method outperforms these algorithms in prediction accuracy by 24.3%, recommendation quality by 33.5% and performance (CPU time) by 45.4%. Moreover, the results showed that 69.5% of the recommended items are truly favorite items to the active user. The remainders 30.5% of the recommended items are potential favorite items.

- by Bushra Alhijawi
- •
- Recommender Systems, Genetic Algorithms, Semantic similarity, Genetic Algorithm
- by Mahendra Pratap
- •
- Image Processing, Machine Learning, Pattern Recognition, Face Recognition

The problem of scheduling a timetable is a complex job that is considered as an NP-hard problem, i.e., not verifiable in polynomial time. This is a typical scheduling problem that appears to be a strenuous job in every academic institute. The Genetic Algorithm, being an adaptive algorithm, can improve its efficiency as it progresses. A timetable should satisfy a particular number of constraints that are specific to the organization that it has been subjected to. Here, the university that is associated with our college, Kerala Technological University (KTU), has just been established and it is essential to have a timetable scheduler of its own to ease the burden of colleges to specify its norms and amount of subjects it deals with every year.

- by IJCSMC Journal
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- Mathematics, Computer Science, Information Technology, Technology

Genetic Algorithm (GA) is a non-parametric optimization technique that is frequently used in problems of combinatory nature with discrete or continuous variables. Depending on the evaluation function used this optimization technique may be applied to solve problems containing more than one objective. In treating with multi-objective evaluation functions it is important to have an adequate methodology to solve the multiple objectives problem so that each partial objective composing the evaluation function is adequately treated in the overall optimal solution. In this paper the multi-objective optimization problem is treated in details and a typical example concerning the allocation of capacitor banks in a real distribution grid is presented. The allocation of capacitor banks corresponds to one of the most important problems related to the planning of electrical distribution networks. This problem consists of determining, with the smallest possible cost, the placement and the dimension of each capacitor bank to be installed in the electrical distribution grid with the additional objectives of minimizing the voltage deviations and power losses. As many other problems of planning electrical distribution networks, the allocation of capacitor banks are characterized by the high complexity in the search of the optimum solution. In this context, the GA comes as a viable tool to obtaining practical solutions to this problem. Simulation results obtained with a electrical distribution grid are presented and demonstrate the effectiveness of the methodology used.

Antenna array is formed by assembly of radiating elements in an electrical or geometrical configuration. In most cases the elements are identical. In this paper proposed a very simple and powerful method for the synthesis of linear array antenna and GA. This method reduced the desired level of side lobe level (SLL) as well as to steer the main beam at different-different angle. A new method for adaptive beam forming for a linear antenna arrays using genetic algorithm (GA) are also proposed. Aditya Sharma | Er. Praveen Kumar Patidar"Review on Linear Array Antenna with Minimum Side Lobe Level Using Genetic Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14544.pdf

Text Document Clustering is one of the fastest growing research areas because of availability of huge amount of information in an electronic form. There are several number of techniques launched for
clustering documents in such a way that documents within a cluster have high intra-similarity and low inter-similarity to other clusters. Many document clustering algorithms provide localized search in
effectively navigating, summarizing, and organizing information. A global optimal solution can be obtained by applying high-speed and high-quality optimization algorithms. The optimization technique performs a
globalized search in the entire solution space. In this paper, a brief survey on optimization approaches to text document clustering is turned out.

Using continuous variables in truss structural optimization results in solutions which have a large number of different cross section sizes whose specific dimensions would in practice be difficult or expensive to create. This approach also creates optimal models which if varied, even slightly, result in structures which do not meet constraint criteria. This research proposes the discretization of cross section sizes to standard sizes of stock produced for the particular cross section and material, and a 1mm precision for node location when using shape optimization. Additionally, Euler buckling constraints are added to all models in order to achieve optimal solutions which can find use in practical application. Several standard test models of trusses from literature, which use continuous variables, are compared to the discrete variable models under the same conditions. Models are optimized for minimal weight using sizing, shape, topology, and combinations of these approaches.

- by Nenad Petrovic and +1
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- Structural Optimization, Topology Optimization, Parametric Design (Architecture), Genetic Algorithm in Optimization

A very widely used drive strategy for PMSM is the field oriented control (FOC), which was proposed in 1971 for induction motors (IMs). However, the FOC scheme is quite complex due to the reference frame transformation and its high dependence upon the motor parameters and speed. To mitigate these problems, a new control strategy for the torque control of induction motor was developed by Takahashi known as the direct torque control (DTC) and by Depenbrock as the direct self control (DSC). The basic direct torque control (DTC) scheme may cause undesired torque, flux and current ripples because of the small number of applicable voltage vectors. The control system should be able to generate any voltage vector, implying the use of space vector modulation (SVM) which complicates the control scheme. The discrete space vector modulation (DSVM) method was proposed for DTC to overcome this problem which replaces the simple switching table by several switching tables, to apply a combination of three voltage vectors in the same sampling period. In this paper, after a brief review of the primary concept of DSVM DTC technique, a new scheme of DSVM DTC for PMSM is proposed with a new set of switching tables taking into account the motor speed and the absolute values of torque and flux feedback errors. In one fixed sampling time interval, three vectors are applied to the motor including the two null vectors. Comparisons of the basic DTC and the improved DSVM DTC schemes are made based on the system performance and switching loss. For this purpose the DSVM technique uses prefixed time intervals within a sampling cycle to synthesize a higher number of voltage vectors than the basic DTC scheme. A set of switching table is carried out to minimize the torque error. An optimal vector selector is developed to reduce the switching loss and make the system more stable. The sampling period does not need to be doubled in order to achieve a mean switching frequency practically equal to that of the basic DTC scheme. For a comparable performance, the switching loss of the proposed scheme is less than that of the basic DTC method. The vector application sequence is investigated and an optimal algorithm is developed to reduce the switching loss and torque ripple. Simulation and experiments on the improved DSVM DTC are carried out and compared with those on the basic DTC scheme.

- by Sakunthala Sidda
- •
- Fuzzy Logic, Genetic Algorithms, Fuzzy Logic Control, Genetic Fuzzy Systems

Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.

Axial flux permanent magnet motors having a dual air-gap configuration have been designed and constructed to achieve high power density and used in industries interestingly and specially in the system of driving electrical vehicles. In this paper, the equation related to the design and dimensions of double-sided slotted axial flux synchronous motor with internal stator (TORUS) will be investigated. Then, an optimum design based on genetic algorithm with the purpose of increasing power density is presented. Two-dimensional finite-element analysis (FEM) is used to demonstrate these optimization and its results will be presented. Finally, it presents the resultant back emf waveform and current for a 1.0 kW, 48 V, 50 Hz, 4-poles/15-slots TORUS slotted axial motor prototype, based on our optimization techniques. KEYWORDS Axial Flux PM Motors (AFPM), Power Density, Genetic Algorithm and Finite Element method (FEM)

- by Rawaa Al-Dabbagh
- •
- Computer Science, Information Technology, Information Security, Risk Reduction

This paper introduces a solution of the economic load dispatch (ELD) problem using a hybrid approach of fuzzy logic and genetic algorithm (GA). The proposed method combines and extends the attractive features of both fuzzy logic and GA. The proposed approach is compared with lambda iteration method (LIM) and GA. The investigation reveals that the proposed approach can provide accurate solution with fast convergence characteristics and is superior to the GA and LIM.

- by iaeme iaeme
- •
- Engineering, Artificial Intelligence, Pid Controller, Genetic Algorithm (GA)

Selecting an optimal threshold value is the most important step in image thresholding algorithms. For a bimodal histogram which can be modeled as a mixture of two Gaussian density functions, estimating these densities in practice is not simply feasible. The objective of this paper is to use adaptive particle swarm optimization (APSO) for the suboptimal estimation of the means and variances of these two Gaussian density functions; then, the computation of the optimal threshold value is straightforward. The comparisons of experimental results in a wide range of complex bimodal images show that this proposed thresholding algorithm presents higher correct detection rate of object and background in comparison to the other methods including Otsu’s method and estimating the parameters of Gaussian density functions using genetic algorithm (GA). Meanwhile, the proposed thresholding method needs lower execution time than the PSO-based method, while it shows a little higher correct detection rate of object and background, with lower false acceptance rate and false rejection rate.

- by IJERA Journal
- •
- Artificial Intelligence, Machine Learning, Data Mining, Genetic Algorithm (GA)

The goal of this study was to investigate a novel approach of predicting the ultimate capacity of axially loaded circular concrete-filled steel tube (CCFST) columns. A hybrid intelligent system, namely GAP-BART, was developed based on the Bayesian additive regression tree (BART) combining with three nature-inspired optimization algorithms such as Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO), and then applied. Three sub-hybrid models of the system were built, abbreviated as G-BART, A-BART, and P-BART, respectively, using 504 experimental data collected from published research. The compiled database covered five input variables, including the diameter of the circular cross-section-section (D), the wall thickness of the steel tube (t), the length of the column (L), the compressive strength of the concrete (f ′ c), and the yield strength of the steel tube (f y). The coefficient of determination (R 2) values of (0.9971, 0.9982, and 0.9986) and (0.9891, 0.9923 and 0.9931) were achieved for training and testing of G-BART, A-BART, and P-BART models, respectively. The P-BART model performed the lowest RMSE and MAE values for the training and testing set of (66.85 kN and 49.60 kN) and (141.24 kN and 102.04 kN), respectively. These results indicated that although the proposed models were able to estimate ultimate axial capacity with high accuracy, the P-BART model had the best performance among them. For benchmarking, the obtained results were validated against several mathematical approaches as well as other AI techniques (MARS, ANN). The findings of the comparative analysis clearly showed superior ability to predict the CFST ultimate axial capacity relative to the benchmark models. The relative importance of each predictor was investigated to find the most significant input variables. The results confirmed that the hybrid GAP-BART system can serve as a reliable and accurate tool for the design of CCFST columns and to predict their performance.

Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which uses static ratios taken from the bank financial statements for prediction, which has its own theoretical advantages. The performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm. This dynamic selection can be accomplished by Genetic Algorithm and it improves the performance of prediction model. .

- by Mohammad Ghiasi
- •
- Computer Science, Power System, Renewable energy resources, Smart Cities

The Nurse scheduling problem (NSP) represents a difficult class of Multi-objective optimization problems consisting of number of interfering objectives between the hospitals and individual nurses. Several constraint-based optimization techniques have been proposed to solve automated nursing scheduling problems in an acceptable computation time but most of these techniques are characterized by premature convergences which inhibit optimal global solution. Thus, a Modified Genetic Algorithm (MGA) was developed to solve Nurse Scheduling Problem. The Modified Genetic Algorithm will be implemented by using Matrix Laboratory (MATLAB) software.

- by Abiodun Emmanuel
- •
- Genetics, Computer Science, Algorithms, Genetic Algorithms

In this paper, a combination ANN/Fuzzy technique is used to design a Novel Fuzzy Single Neuron PID (NFSNPID) controller to achieve high performance brushless DC motor. The design steps include two parts. The first part uses the genetic algorithm (GA) to find the optimum parameters of Single Neuron PID (SNPID) controller, while the former deals with the design of fuzzy logic control to update the weights of SNPID control online. To demonstrate the designed controller effectiveness, a comparative study is made with between the NFSNPID, Conventional Fuzzy Single Neuron PID CFSNPID and SNPID. All controllers were used to drive, separately, the brushless DC motor against the sudden change of load and operating speed. The performed simulations show better results that motivate for further investigations.

- by IRJCS: : International Research Journal of Computer Science and +1
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- Genetics, Algorithms, Genetic Algorithms, Nurse Education

A pseudo random carrier pulse width modulation (PRPWM) scheme for multilevel
power converters, for the elimination of harmonic distortion is presented. This paper
also proposes a cascaded H-bridge modified five level inverter topology with less
number of switches features a high modularity degree compared to conventional
multilevel inverter topologies. PRPWM scheme is optimized using a binary valued
genetic algorithm (GA). The proposed optimized PRPWM strategy provides an
improved performance over traditional random pulse width modulation scheme for
multilevel inverters. The total harmonic distortion (THD) is taken as the performance
index for optimization. The proposed scheme for generating optimized PRPWM for
modified multilevel inverter is simulated for a three-level and five-level inverter, and
experimental results are presented for a five-level inverter.

This paper presents a new algorithm for solving unit commitment (UC) problems using a binary-real coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems , in which some power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. In the proposed algorithm, the algorithm integrates the main features of a binary-real coded genetic algorithm (GA) and k-means clustering technique. The binary coded GA is used to obtain a feasible commitment schedule for each generating unit; while the power amounts generated by committed units are determined by using real coded GA for the feasible commitment obtained in each interval. k-means clustering algorithm divides population into a specific number of subpopulations with dynamic size. In this way, using k-means clustering algorithm allows the use of different GA operators with the whole population and avoids the local problem minima. The effectiveness of the proposed technique is validated on a test power system available in the literature. The proposed algorithm performance is found quite satisfactory in comparison with the previously reported results.

- by M. A. El-Shorbagy
- •
- Bioinformatics, Computer Science, Artificial Intelligence, Machine Learning

The surface roughness is a widely used index of product quality in terms of precision fit of mating surfaces, fatigue life improvement, corrosion resistance, aesthetics, etc. Surface roughness also denotes the amount of energy and other resources consumed during machining. This paper presents an approach for determining the optimum machining parameters leading to minimum surface roughness by integrating Artificial Neural Network(ANN) and Genetic Algorithm (GA). To check the capability of the ANN-GA approach for prediction and optimization of surface roughness, a real machining experiment has been referred in this study. A feed forward neural network is developed by collecting the data obtained during the turning of Ti-6Al-4 V titanium alloy. The MATLAB toolbox has been used for training and testing of neural network model. The predicted results using ANN indicate good agreement between the predicted values and experimental values. Further, GA is integrated with neural network model to determine the optimal machining parameters leading to minimum surface roughness. The analysis of this study proves that the ANN-GA approach is capable of predicting the optimum machining parameters.

- by Sachin Saxena and +1
- •
- hybrid ANN-GA, Genetic Algorithm (GA)

In Cloud computing environments, computing resources are
available for users, and they only pay for used resources The most important issues in cloud computing are scheduling and energy consumption which many researchers worked on them. In these systems a scheduling mechanism has two phases: task prioritization and processor selection. Di fferent priorities may cause to di fferent makespan and for each processor which assigned to the task, the energy consumption is di fferent. So
a good scheduling algorithm must assign priority to each task and select the best processor for them, in such a way that makespan and energy consumption be minimized. In this paper, we proposed a two phase's algorithm for scheduling, named TETS, the first phase is task prioritization and the second phase is processor assignment.We use three prioritization methods for prioritize the tasks and produce optimized initial chromosomes and assign the tasks to processors which is an energy-aware model. Simulation results indicate that our algorithm is better than previous algorithms in terms of energy consumption and makespan. It can improve
the energy consumption by 20% and makespan by 4%.

- by Mohammad Shojafar
- •
- Genetics, Computer Science, Algorithms, Distributed Computing

This paper presents an enhanced nonlinear PID (NPID) controller to follow a preselected speed profile of brushless DC motor drive system. This objective should be achieved regardless the parameter variations, and external disturbances. The performance of enhanced NPID controller will be investigated by comparing it with linear PID control and fractional order PID (FOPID) control. These controllers are tested for both speed regulation and speed tracking. The optimal parameters values of each control technique were obtained using Genetic Algorithm (GA) based on a certain cost function. Results shows that the proposed NPID controller has better performance among other techniques (PID and FOPID controller).

Distributed Generations (DGs) have a productive capacity of tens of kilowatts to several megawatts, which are used to produce electrical energy at close proximity to consumers, which of the types of DGs can be named solar cells and Photovoltaics (PVs), fuel cells, micro turbines, wind power plants, and etc. If such kinds of power plants are connected to the network in optimal places, they will have several positive effects on the system, such as reducing network losses, improving the voltage profile, and increasing network reliability. The lack of optimal placement of DGs in the network will increase the costs of energy production and losses in transmission lines. Therefore, it is necessary to optimize the location of such DGs in the network so that the number of DGs, installation locations, and their capacity are determined to which the maximum reduction in network losses occurs. Besides, by applying an appropriate objective function, the evolutionary algorithm can find the optimal location of renewable units with respect to the constraints of the issue. In this paper, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to address the placement of wind and photovoltaic generators simultaneously in two states: With and without considering the effects of greenhouse gas emission. In this regard, first, an analytical method for optimal DG (wind and PV) placement is presented, then, the proposed approach is applied over a real study case, and the simulation carried out using the MATLAB program; hence, the placement problem was solved using GA and PSO and implemented in the IEEE 33-bus radial distribution system. The obtained results were compared and analyzed. The results of the simulation show the improvement of the voltage profile and the reduction of losses in the network.

Abstract— Mobile Ad hoc Network (MANET) is a kind of self configuring and self describing wireless ad hoc networks. MANET has characteristics of topology dynamics due to factors such as energy conservation and node movement that leads to dynamic load balanced clustering problem (DLBCP). It is necessary to have an effective clustering algorithm for adapting the topology change. Generally, Clustering is mainly used to reduce the topology size. In this, we used load balance and energy metric in GA to solve the DLBCP. It is important to select the energy efficient cluster head for maintaining the cluster structure and balance the load effectively. Elitism based Immigrants Genetic algorithm (EIGA) and Memory Enhanced Genetic Algorithm (MEGA) are used to solve DLBCP. These schemes will select the optimal cluster head by considering the parameters includes distance and energy. We used EIGA to maintain the diversity level of the population and memory scheme (MEGA) to store the old environments into the memory. It promises the energy efficiency for the entire cluster structure to increase the lifetime of the network. The experimental results show that the proposed schemes increases the network life time and reduces the energy consumption.

- by IJTET Journal
- •
- Manet, Genetic Algorithm (GA), Dlbcp, Dynamic Ga

Genetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I).

- by mohammad-mehdi Khani
- •
- Genetics, Genetic Algorithm, Surface Roughness, Cutting Force

—This paper presents a new approach based on genetic algorithms (GAs) to generate maximal frequent itemsets (MFIs) from large datasets. This new algorithm, GeneticMax, is heuristic which mimics natural selection approaches for finding MFIs in an efficient way. The search strategy of this algorithm uses a lexicographic tree that avoids level by level searching which reduces the time required to mine the MFIs in a linear way. Our implementation of the search strategy includes bitmap representation of the nodes in a lexicographic tree and identifying frequent itemsets (FIs) from superset-subset relationships of nodes. This new algorithm uses the principles of GAs to perform global searches. The time complexity is less than many of the other algorithms since it uses a non-deterministic approach. We separate the effect of each step of this algorithm by experimental analysis on real datasets such as Tic-Tac-Toe, Zoo, and a 10000×8 dataset. Our experimental results showed that this approach is efficient and scalable for different sizes of itemsets. It accesses a major dataset to calculate a support value for fewer number of nodes to find the FIs even when the search space is very large, dramatically reducing the search time. The proposed algorithm shows how evolutionary method can be used on real datasets to find all the MFIs in an efficient way.

A way to enhance the performance of a model that combines genetic algorithms and fuzzy logic for feature selection and classification is proposed. Early diagnosis of any disease with less cost is preferable. Diabetes is one such disease. Diabetes has become the fourth leading cause of death in developed countries and there is substantial evidence that it is reaching epidemic proportions in many developing and newly industrialized nations. In medical diagnosis, patterns consist of observable symptoms along with the results of diagnostic tests. These tests have various associated costs and risks.
In the automated design of pattern classification, the proposed system solves the feature subset selection problem. It is a task of identifying and selecting a useful subset of pattern-representing features from a larger set of features. Using fuzzy rule-based classification system, the proposed system proves to
improve the classification accuracy.

"Dos de las técnicas más ampliamente utilizadas en el campo del reconocimiento de rostros con imágenes infrarrojas son PCA (Principal Component Analisys) y LDA (Linear Discriminant Analysis). En este trabajo se presentan los resultados obtenidos al emplear algoritmos genéticos para incrementar el poder discriminante de los vectores que conforman el espacio de características generado por dichas técnicas, por medio de la asignación ponderada de pesos a cada vector según su nivel de aporte en la etapa de clasificación. Se muestra que bajo el esquema propuesto, se obtiene un menor error de clasificación respecto al método convencional"

- by Revista Ingeniería y Ciencia
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- Engineering, Genetics, Mathematics, Algebra
- by C. A. Oyeleye
- •
- Genetics, Computer Science, Algorithms, Genetic Algorithms

In this paper, comparative study of two approaches, Genetic Algorithm (GA) and Lambda Iteration method (LIM) have been used to provide the solution of the economic load dispatch (ELD) problem. The ELD problem is defined as to minimize the total operating cost of a power system while meeting the total load plus transmission losses within generation limits. GA and LIM have been used individually for solving two cases, first is three generator test system and second is ten generator test system. The results are compared which reveals that GA can provide more accurate results with fast convergence characteristics and is superior to LIM.

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN...

The torque measurement applied to the joints of the lower limbs during sitting and getting up was investigated by dynamic optimization due to this task's various applications, including educational, therapeutic, sports applications, and the construction of active orthoses and prostheses. The kinematic data of a healthy male were sampled to perform sitting and standing movements. After completing several initial cycles, in one process, the angles of the knee joint and ankle joint, along with the rate of change of these angles, were measured experimentally. A double inverted pendulum with two degrees of freedom was considered as a skeletal model. Two equivalent flexion and extension muscles enhanced by a model were also considered the muscular models on each knee and ankle joint. As a new look at sitting and standing, by accepting simplistic assumptions, the activation of the equivalent muscles in each joint, as a function of the modified joint equilibrium angle, and the rate of change of angle (angular velocity) of the modified joint were proposed. Therefore, these variables' constant coefficients in a tracking problem must be optimized to achieve each muscle's activity. The genetic algorithm was used to optimize these coefficients along with the coefficients related to the model. The results showed that the major muscle activity is related to the knee flexor during sitting and standing, and the minor muscle activity is associated with the ankle extensor muscle.

The ever Increasing progress of a network-distributed computing and particularly the rapid development of the web have had a broad impact on society. Online delivery of educational instructions provides the opportunity to bring the colleges and universities simply use the online infrastructure for institutions and students. The main aim of this paper is to introduce to find similar patterns of use in the data gathered from Learning Online Network with Computer-Assisted Personalized Approach (LON-CAPA), and eventually be able to make predictions as to the mostbeneficial course of studies for each learner based on their present usage. The system could then make suggestions to the learner as to how to best proceed. The objective is to predict the students’ final grades based on their web-use features, which are extracted from the homework data. Using a GA to optimize a combination of classifiers test data we selected the student and course data of a LON-CAPA course, we design, implement, and evaluate a series of pattern classifiers with various parameters in order to compare their performance on a dataset from LON-CAPA.

In this paper, an evolutionary genetic algorithm is used to generate face sketch from the face description. Face sketch generation without face image is extremely important for the law enforcement agencies. The genetic algorithm is used for generating face sketch through several iterations of the algorithm. The face image description is captured through graphical user interface just by clicking options for each face features. Face features are used to extract face images and generate initial population for the genetic algorithm. Genetic operators such as selection, crossover and mutation are used for next generation of the population. The Genetic algorithm cycle is repeated until the user is satisfied with face sketch generated. The novelty of the paper includes face sketch generation from face image description. The result shows that evolutionary based technique for sketch generation produces the desired face sketch.

The authors deal with the topic of the final assembly scheduling realized by the use of genetic algorithms (GAs). The objective of the research was to study in depth the use of GA for scheduling mixed-model assembly lines and to propose a model able to produce feasible solutions also according to the particular requirements of an important Italian motorbike company, as well as to capture the results of this change in terms of better operational performances. The " chessboard shifting " of work teams among the mixed-model assembly lines of the selected company makes the scheduling problem more complex. Therefore, a complex model for scheduling is required. We propose an application of the GAs in order to test their effectiveness to real scheduling problems. The high quality of the final assembly plans with high adherence to the delivery date, obtained in a short elaboration time, confirms that the choice was right and suggests the use of GAs in other complex manufacturing systems.

- by Abiodun Emmanuel
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- Genetics, Computer Science, Algorithms, Genetic Algorithms

An approach to the structural health management (SHM) of future aerospace vehicles is presented. Such systems will need to operate robustly and intelligently in very adverse environments, and be capable of self-monitoring (and ultimately, self-repair). Networks of embedded sensors, active elements, and intelligence have been selected to form a prototypical "smart skin" for the aerospace structure, and a methodology based on multi-agent networks developed for the system to implement aspects of SHM by processes of self-organisation. Problems are broken down with the aid of a "response matrix" into one of three different scenarios: critical, sub-critical, and minor damage. From these scenarios, three components are selected, these being: (a) the formation of "impact boundaries" around damage sites, (b) self-assembling "impact networks", and (c) shape replication. A genetic algorithm exploiting phase transitions in systems dynamics has been developed to evolve localised algorithms for impact boundary formation, addressing component (a). An ant colony optimisation (ACO) algorithm, extended by way of an adaptive dead reckoning scheme (ADRS) and which incorporates a "pause" heuristic, has been developed to address (b). Both impact boundary formation and ACO-ADRS algorithms have been successfully implemented on a "concept demonstrator", while shape replication algorithms addressing component (c) have been successfully simulated.