A Template Matching Method Based on Genetic Algorithm Optimization

2012 ◽  
Vol 220-223 ◽  
pp. 1298-1302 ◽  
Author(s):  
Xiao Hui Zhang ◽  
Qing Liu ◽  
Mu Li
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Template Matching ◽  
Fitness Function ◽  
Image Process ◽  
Algorithm Optimization ◽  
Matching Method ◽  
Correlation Degree ◽  
Degree Function ◽  
Effective Performance

This paper presents a method of using Genetic Algorithm (GA) to optimize template and image searching process, using template matching to recognize target. An initial matching template is set manually according to 2D shape and the optimizing template is obtained by GA optimizing to meet the requirement of real-time and effective performance. Then the pixel position is encoded into genes, template correlation degree function works as fitness function to do GA search to recognize the target. The relating image process experiments show that this method has good real-time and robustness performance.

scholarly journals Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1297
Author(s):  
Md. Shabiul Islam ◽  
Most Tahamina Khatoon ◽  
Kazy Noor-e-Alam Siddiquee ◽  
Wong Hin Yong ◽  
Mohammad Nurul Huda
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Real Time ◽  
Large Scale ◽  
Fitness Function ◽  
Random Search ◽  
Linear Equations ◽  
Simultaneous Equations ◽  
Function Evaluation ◽  
Solution Sets

Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

A Bi-objective Genetic Algorithm Optimization of Chaos-DNA Based Hybrid Approach

2019 ◽  
Vol 28 (2) ◽  
pp. 333-346 ◽  
Author(s):  
Shelza Suri ◽  
Ritu Vijay
Keyword(s):  
Genetic Algorithm ◽  
Image Encryption ◽  
Fitness Function ◽  
Hybrid Approach ◽  
Good Choice ◽  
Algorithm Optimization ◽  
Change Rate ◽  
Fitness Functions ◽  
Significant Performance ◽  
The Given

Abstract The paper implements and optimizes the performance of a currently proposed chaos-deoxyribonucleic acid (DNA)-based hybrid approach to encrypt images using a bi-objective genetic algorithm (GA) optimization. Image encryption is a multi-objective problem. Optimizing the same using one fitness function may not be a good choice, as it can result in different outcomes concerning other fitness functions. The proposed work initially encrypts the given image using chaotic function and DNA masks. Further, GA uses two fitness functions – entropy with correlation coefficient (CC), entropy with unified average changing intensity (UACI), and entropy with number of pixel change rate (NPCR) – simultaneously to optimize the encrypted data in the second stage. The bi-objective optimization using entropy with CC shows significant performance gain over the single-objective GA optimization for image encryption.

scholarly journals Flight Motion Controller Design using Genetic Algorithm for a Quadcopter

2018 ◽  
Vol 51 (3-4) ◽  
pp. 59-64 ◽  
Author(s):  
Huu Khoa Tran ◽  
Thanh Nam Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Controller Design ◽  
Fitness Function ◽  
Absolute Error ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Time Operation ◽  
Real Time Operation ◽  
Effective Stability

In this study, the Genetic Algorithm operability is assigned to optimize the proportional–integral–derivative controller parameters for both simulation and real-time operation of quadcopter flight motion. The optimized proportional–integral–derivative gains, using Genetic Algorithm to minimum the fitness function via the integral of time multiplied by absolute error criterion, are then integrated to control the quadcopter flight motion. In addition, the proposed controller design is successfully implemented to the experimental real-time flight motion. The performance results are proven that the highly effective stability operation and the reliable of waypoint tracking.

scholarly journals A fast template matching algorithm based on principal orientation difference

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877822 ◽  
Author(s):  
Jichao Jiao ◽  
Xin Wang ◽  
Zhongliang Deng ◽  
Jichang Cao ◽  
Weihua Tang
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Feature Points ◽  
Matching Method ◽  
Matching Algorithm ◽  
Training Samples ◽  
Illumination Changes ◽  
Orientation Difference ◽  
Direction Information ◽  
State Of Art

In the case that the background scene is dense map regularization complex and the detected objects are low texture, the method of matching according to the feature points is not applicable. Usually, the template matching method is used. When training samples are insufficient, the template matching method gets a worse detection result. In order to resolve the problem stably in real time, we propose a fast template matching algorithm based on the principal orientation difference feature. The algorithm firstly obtains the edge direction information by comparing the images that are binary. Then, the template area is divided where the different features are extracted. Finally, the matching positions are searched around the template. Experiments on the videos whose speed is 30 frames/s show that our algorithm detects the low-texture objects in real time with a matching rate of 95%. Compared with other state-of-art methods, our proposed method reduces the training samples significantly and is more robust to the illumination changes.

scholarly journals Hardware Genetic Algorithm Optimization by Critical Path Analysis using a Custom VLSI Architecture

2015 ◽  
Vol 56 ◽  
Author(s):  
Farouk Smith ◽  
Allan Edward Van den Berg
Keyword(s):  
Genetic Algorithm ◽  
Critical Path ◽  
Fitness Function ◽  
Vlsi Architecture ◽  
Digital Circuit ◽  
Direct Access ◽  
Algorithm Optimization ◽  
Modular Evolution ◽  
Depth Analysis ◽  
Evolutionary Hardware

This paper propose a Virtual-Field Programmable Gate Array (V-FPGA) architecture that allows direct access to its configuration bits to facilitate hardware evolution, thereby allowing any combinational or sequential digital circuit to be realized. By using the V-FPGA, this paper investigates two possible ways of making evolutionary hardware systems more scalable: by optimizing the system’s genetic algorithm (GA); and by decomposing the solution circuit into smaller, evolvable sub-circuits. GA optimization is done by: omitting a canonical GA’s crossover operator (i.e. by using a 1+λ algorithm); applying evolution constraints; and optimizing the fitness function. A noteworthy contribution this research has made is the in-depth analysis of the phenotypes’ CPs. Through analyzing the CPs, it has been shown that a great amount of insight can be gained into a phenotype’s fitness. We found that as the number of columns in the Cartesian Genetic Programming array increases, so the likelihood of an external output being placed in the column decreases. Furthermore, the number of used LEs per column also substantially decreases per added column. Finally, we demonstrated the evolution of a state-decomposed control circuit. It was shown that the evolution of each state’s sub-circuit was possible, and suggest that modular evolution can be a successful tool when dealing with scalability.

scholarly journals Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Gilseung Ahn ◽  
Sun Hur
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Real Time ◽  
Fitness Function ◽  
Cloud Manufacturing ◽  
Genetic Operators ◽  
Real Time Scheduling ◽  
Chromosome Representation ◽  
Time Scheduling ◽  
The Mathematical Model

In cloud manufacturing, customers register customized requirements, and manufacturers provide appropriate services to complete the task. A cloud manufacturing manager establishes manufacturing schedules that determine the service provision time in a real-time manner as the requirements are registered in real time. In addition, customer satisfaction is affected by various measures such as cost, quality, tardiness, and reliability. Thus, multiobjective and real-time scheduling of tasks is important to operate cloud manufacturing effectively. In this paper, we establish a mathematical model to minimize tardiness, cost, quality, and reliability. Additionally, we propose an approach to solve the mathematical model in a real-time manner using a multiobjective genetic algorithm that includes chromosome representation, fitness function, and genetic operators. From the experimental results, we verify whether the proposed approach is effective and efficient.

Improving Genetic Algorithm Based Parameter Optimizing LSSVM Model for Tyre Regression

2011 ◽  
Vol 55-57 ◽  
pp. 1839-1844
Author(s):  
Xiao Long Zhang ◽  
Liang Li ◽  
Jian Song ◽  
Dan Dan Sheng
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Test Data ◽  
Fitness Function ◽  
Bench Test ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Risk Minimization ◽  
Simulation And Control ◽  
And Control

According to the requirement of vehicle dynamics' accurate simulation and control, the paper studies the tyre regression algorithm based on the tyre bench test data. Due to the tyre test's characters of few data and big discreteness, the method of least squares support vector regression (LSSVM), which has the superiority of structural risk minimization, was selected to find the nonlinear mapping between input and output variables of tyre test data. Removing data gross error and improving the sparsification measures were taken to increase the calculation real time of standard LSSVM algorithm. An adaptive genetic algorithm (AGA) with global searching ability was chosen to determine the kernel function and regularization parameters of LSSVM. Test data set’s regression root mean square error (RMSE) was taken as the fitness function of AGA. Finally, the tyre test data under steady state sideslip condition was provided to simulate and verify the effectiveness of tyre regression algorithm, according to the precision and real time requirements of vehicle dynamic simulation and control.

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