Optimal Parameter Settings for the (1 + λ, λ) Genetic Algorithm

2016 ◽  
Author(s):  
Benjamin Doerr
Keyword(s):  
Genetic Algorithm ◽  
Optimal Parameter

scholarly journals Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast

2006 ◽  
Vol 24 (12) ◽  
pp. 3185-3189 ◽  
Author(s):  
Y. H. Lee ◽  
S. K. Park ◽  
D.-E. Chang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Parameter ◽  
Fitness Function ◽  
Convective Available Potential Energy ◽  
Reduction Rate ◽  
Cumulus Parameterization ◽  
Precipitation Forecast ◽  
Physical Parameters ◽  
Meteorological Model ◽  
Filter Parameter

Abstract. In this study, optimal parameter estimations are performed for both physical and computational parameters in a mesoscale meteorological model, and their impacts on the quantitative precipitation forecasting (QPF) are assessed for a heavy rainfall case occurred at the Korean Peninsula in June 2005. Experiments are carried out using the PSU/NCAR MM5 model and the genetic algorithm (GA) for two parameters: the reduction rate of the convective available potential energy in the Kain-Fritsch (KF) scheme for cumulus parameterization, and the Asselin filter parameter for numerical stability. The fitness function is defined based on a QPF skill score. It turns out that each optimized parameter significantly improves the QPF skill. Such improvement is maximized when the two optimized parameters are used simultaneously. Our results indicate that optimizations of computational parameters as well as physical parameters and their adequate applications are essential in improving model performance.

Mill Gear Box of Intelligent Diagnosis Based on Support Vector Machine Parameters Optimization

2014 ◽  
Vol 697 ◽  
pp. 239-243 ◽  
Author(s):  
Xiao Hui Liu ◽  
Yong Gang Xu ◽  
De Ying Guo ◽  
Fei Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Rolling Mill ◽  
Optimal Parameter ◽  
Structural Parameters ◽  
Parameters Optimization ◽  
Support Vector ◽  
Intelligent Diagnosis ◽  
Gear Box ◽  
Parameter Values

For mill gearbox fault detection problems, and puts forward combining support vector machine (SVM) and genetic algorithm, is applied to rolling mill gear box fault intelligent diagnosis methods. The choice of parameters of support vector machine (SVM) is a very important for the SVM performance evaluation factors. For the selection of structural parameters of support vector machine (SVM) with no theoretical support, select and difficult cases, in order to reduce the SVM in this respect, puts forward the genetic algorithm to optimize parameters, and the algorithm of the model is applied to rolling mill gear box in intelligent diagnosis, using the global searching property of genetic algorithm and support vector machine (SVM) of the optimal parameter values. Results showed that the suitable avoided into local solution optimization, the method to improve the diagnostic accuracy and is a very effective method of parameter optimization, and intelligent diagnosis for rolling mill gear box provides an effective method.

Optimization of RF Magnetron Sputtering Process Parameters Based on Genetic Algorithm and Neural Network

2015 ◽  
Vol 1109 ◽  
pp. 481-485
Author(s):  
Norlina Mohd Sabri ◽  
Nor Diyana Md Sin ◽  
Mazidah Puteh ◽  
Mohamad Rusop
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Magnetron Sputtering ◽  
Band Gap ◽  
Optical Band Gap ◽  
Optimal Parameter ◽  
Rf Magnetron Sputtering ◽  
Process Parameter ◽  
Thin Film Fabrication ◽  
Parameter Combination

A procedure for RF magnetron sputtering process parameter optimization is proposed in this paper. This study has been focusing on determining the optimal parameter combination for producing the desirable optical band gap. In this proposed procedure, Genetic Algorithm (GA) has been adapted as the optimization tool, while Artificial Neural Network (ANN) has been implemented as the prediction model. GA was adapted to search for the optimal parameter combination from the set of parameters, while later the ANN modeling had been utilized to predict the optical band gap energies for each of the parameter combinations. The result from the GA optimization is expected to produce the highest band gap value. The computational results from the proposed procedure were then compared with the actual laboratory experimental results from the ZnO thin film fabrication. Based on the comparison result, the performance of the proposed procedure had proven to be promising in determining the most optimized process parameter combination from the set of parameters.

scholarly journals Optimal parameter estimation for a DC motor using genetic algorithm

2020 ◽  
Vol 11 (2) ◽  
pp. 1047 ◽  
Author(s):  
Mohammad Soleimani Amiri ◽  
Mohd Faisal Ibrahim ◽  
Rizauddin Ramli
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Parameter Estimation ◽  
Numerical Analysis ◽  
Optimal Parameter ◽  
Dc Motor ◽  
Linear Features ◽  
Estimated Parameters ◽  
Optimal Parameter Estimation ◽  
Non Linear

Estimating the parameters of a geared DC motor is crucial in terms of its non-linear features. In this paper, parameters of a geared DC motor are estimated genetically. Mathematical model of the DC motor is determined by Kirchhoff’s law and dynamic model of its shafts and gearbox. Parameters of the geared DC motor are initially estimated by MATLAB/SIMULINK. The estimated parameters are defined as initial values for Genetic Algorithm (GA) to minimize the error of the simulated and actual angular trajectory captured by an encoder. The optimal estimated model of the geared DC motor is validated by different voltages as the input of the actual DC motor and its mathematical model. The results and numerical analysis illustrate it can be ascertained that GA is appropriate to estimate the parameters of platforms with non linear characteristics.

scholarly journals Feature Optimization for Gait Phase Estimation with a Genetic Algorithm and Bayesian Optimization

2021 ◽  
Vol 11 (19) ◽  
pp. 8940
Author(s):  
Wonseok Choi ◽  
Wonseok Yang ◽  
Jaeyoung Na ◽  
Giuk Lee ◽  
Woochul Nam
Keyword(s):  
Genetic Algorithm ◽  
Time Domain ◽  
Time Window ◽  
Optimal Parameter ◽  
Estimation Error ◽  
Optimization Techniques ◽  
Bayesian Optimization ◽  
Series Data ◽  
Phase Estimation ◽  
Gait Phase

For gait phase estimation, time-series data of lower-limb motion can be segmented according to time windows. Time-domain features can then be calculated from the signal enclosed in a time window. A set of time-domain features is used for gait phase estimation. In this approach, the components of the feature set and the length of the time window are influential parameters for gait phase estimation. However, optimal parameter values, which determine a feature set and its values, can vary across subjects. Previously, these parameters were determined empirically, which led to a degraded estimation performance. To address this problem, this paper proposes a new feature extraction approach. Specifically, the components of the feature set are selected using a binary genetic algorithm, and the length of the time window is determined through Bayesian optimization. In this approach, the two optimization techniques are integrated to conduct a dual optimization task. The proposed method is validated using data from five walking and five running motions. For walking, the proposed approach reduced the gait phase estimation error from 1.284% to 0.910%, while for running, the error decreased from 1.997% to 1.484%.

Optimization of Bayesian Filters and Hammerstein-Wiener Models for EMG-Force Signals Using Genetic Algorithm

2009 ◽  
Author(s):  
Anish Sebastian ◽  
Parmod Kumar ◽  
Madhavi Anugolu ◽  
Marco P. Schoen ◽  
Alex Urfer ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
System Identification ◽  
Optimal Parameter ◽  
Nonlinear Models ◽  
Human Hand ◽  
Identification System ◽  
Force Estimation ◽  
Emg Signal ◽  
Wiener Models ◽  
Bayesian Filters

Processing electromyographic (EMG) signals for force estimation has many unknown variables that can influence the outcome or interpretation of the recorded EMG signal significantly. An array of filtering methods have been proposed over the past few years with the objective to classify motion for use in prosthetic hands. In this paper, we explore the optimal parameter settings of a set of Bayesian based EMG filters with the objective to use the filtered EMG data for system identification. System identification is utilized to establish a relationship between the measured EMG data and the generated force developed by fingers in a human hand. The proposed system identification is based on nonlinear Hammerstein-Wiener models. Optimization is also applied to find the optimal parameter settings for these nonlinear models. Genetic Algorithm (GA) is used to conduct the optimization for both, the optimal parameter settings for the Bayesian filters as well as the Hammerstein-Wiener model. The experimental results and optimization analysis indicate that the optimization can yield significant improvement in data accuracy and interpretation.

Beta Chaotic Map Based Image Encryption Using Genetic Algorithm

2018 ◽  
Vol 28 (11) ◽  
pp. 1850132 ◽  
Author(s):  
Manjit Kaur ◽  
Vijay Kumar
Keyword(s):  
Genetic Algorithm ◽  
Image Encryption ◽  
Optimal Parameter ◽  
Fitness Function ◽  
Chaotic Map ◽  
Input Image ◽  
Experimental Results ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Computational Speed

In this paper, an efficient image encryption technique using beta chaotic map, nonsubsampled contourlet transform, and genetic algorithm is proposed. Initially, the nonsubsampled contourlet transform is utilized to decompose the input image into subbands. The beta chaotic map is used to develop pseudo-random key that encrypts the coefficients of subbands. However, it requires certain parameters to encrypt these coefficients. A multiobjective fitness function for genetic algorithm is designed to find the optimal parameter of beta chaotic map. The inverse of nonsubsampled contourlet transform is performed to obtain a ciphered image. The performance of the proposed technique is compared with recently developed well-known meta-heuristic based image encryption techniques. Experimental results reveal that the proposed technique provides better computational speed and high encryption intensity. The comparative analyses show effectiveness of the proposed image encryption technique.

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