Genetic Algorithm For Application Research Articles

Educational Intelligent System Using Genetic Algorithm

Abstract This paper presents an Intelligent Information System for education. The system was created for teaching students to use genetic algorithm in application to optimization tasks. The system allows to quickly encode a solution of the problem and pick up most suitable configuration of genetic algorithm. The paper also demonstrates a specific example of usage of educational system to solve an optimization task. The paper contains a description of the educational system and its features, a description of its capabilities of working with genetic algorithm and its graphical interface.

Implementasi Algoritma Genetika Pada Perancangan Aplikasi Android Untuk Memprediksi Buta Warna

Color blindness is one of the decreasing diseases which is very difficult to determine its deterioration, whether a family member will suffer from color blindness or not, especially with the prediction of color blindness which has been using manual methods by calculating the inheritance formula. Genetic Algorithms have advantages over other traditional optimization algorithms. To implement a computerized method in predicting color blindness that can be used by many people, it is necessary to have a user-friendly operating system like the Android operating system. The test results show that the implementation of genetic algorithms in applications to predict color blindness produces more optimal predictions and their application to Android makes the application a user friendly application. Keywords: Heredity, Color Blindness, Genetic Algorithms, Android Application, Color Blindness Predictions

EVALUATION OF THE OPERATORS OF THE GENETIC ALGORITHM IN APPLICATION ON THE TRAVELING SALESMAN PROBLEM.

02Feb 2018 EVALUATION OF THE OPERATORS OF THE GENETIC ALGORITHM IN APPLICATION ON THE TRAVELING SALESMAN PROBLEM. S. Bourazza. Mathematics Department, faculty of Science, Jazan University, KSA.

Improved Quantum Genetic Algorithm in Application of Scheduling Engineering Personnel

To verify the availability of the improved quantum genetic algorithm in solving the scheduling engineering personnel problem, the following work has been carried out: the characteristics of the scheduling engineering personnel problem are analyzed, the quantum encoding method is proposed, and an improved quantum genetic algorithm is applied to address the issue. Taking the low efficiency and the bad performance of the conventional quantum genetic algorithm into account, a universal improved quantum genetic algorithm is introduced to solve the scheduling engineering personnel problem. Finally, the examples are applied to verify the effectiveness and superiority of the improved quantum genetic algorithm and the rationality of the encoding method.

Accurate Designing of Flat-Walled Multi-Layered Lining System Using Genetic Algorithm for Application in Anechoic Chambers

In anechoic chambers, the flat-walled multilayered acoustic lining systems are cheaper and easier to install than conventional wedge type systems. Sequence, material and thickness of layers are all design variables. By choosing the right configuration, the acoustical performance and overall thic- kness of flat-walled multilayered systems can be comparable to conventional wedge systems. In this study, the materials considered include air and poroelastic materials such as polyurethane foams, melamine foams, and glass wool. In order to evaluate acoustical performance of a given configurati- on, the poroelastic materials are modeled using Biot’s formulations instead of a simpler impedance method to get more accurate results. A genetic algorithm implemented within ModeFRONTIER opti- mization software was used to select configurations which have a cut-off frequency of 100 Hz or less. The configuration that met this requirement with the smallest overall thickness was determined optimal. This configuration has an overall thickness of 65.8 cm and is composed of 4 different polyurethane foams. Since a considerable difference was observed between the cut-off frequencies obtained using Biot’s model and the simpler impedance method, this justifies the use of Biot’s model in the optimization.

Neural network & genetic algorithm in application to handwritten character recognition

Neural network & genetic algorithm in application to handwritten character recognition

Optimization Methods for Optimal Power Quality Monitor Placement in Power Systems: A Performance Comparison

This paper presents a performance comparison between three optimization techniques, namely, quantum-inspired binary particle swarm optimization, binary particle swarm optimization and genetic algorithm in application to optimal power quality monitor (PQM) placement method for voltage sag assessment. The optimization handles the observability constraints based on the topological monitor reach area concept and solves a multi-objective function in obtaining the optimal number and placement of PQMs in power systems. The objective function consists of two functions which are based on monitor overlapping index and sag severity index. All the optimization algorithms have been implemented and tested on the IEEE 34-node, the 69-bus and the IEEE 118-bus test systems to evaluate the effectiveness of the aforementioned techniques. The results show that QBPSO provide a better optimal solution than the standard binary particle swarm optimization and the existing genetic algorithm by 56% and 31%, respectively. The validation test illustrated that the optimal PQM placements can detect and record the voltage sag events due to any fault occurrence in the systems.

A genetic algorithm to simultaneously retrieve land surface roughness and soil wetness

Two key parameters to affect microwave backscattering from the land surface are the surface roughness and soil wetness. A novel genetic algorithm is developed for multi-parameter retrieval of land surface roughness and soil wetness from angular backscattering observations. Parameters of wetness and roughness are encoded into genes. Genes are constituents of chromosomes, which undergo optimal selection based on a natural evolutionary process in the genetic algorithm. The theoretical model of a two-scale rough surface is employed for computation of the cost function. Results retrieved using this genetic algorithm are compared well with ground data measurements. This study presents an example of the genetic algorithm for application of multi-parameter retrieval in remote sensing.