A Genetic Algorithm-Based Quasi-Linear Regression Method and Application

2013 ◽  
Vol 347-350 ◽  
pp. 2700-2705
Author(s):  
Fa Chao Li ◽  
Ke Na Zhang
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Regression Model ◽  
Search Algorithm ◽  
Linear Regression Method ◽  
Computational Mathematics ◽  
Short Term Prediction ◽  
Optimization Search ◽  
Important Branch ◽  
Scientific Prediction

Regression analysis, as an important branch of statistics, is an effective tool for scientific prediction. Genetic algorithm is an optimization search algorithm in computational mathematics. In this paper, a new regression model named quasi-linear regression model is established. Further, its implementation method is introduced in detail. Then by taking the population development of Hebei province as an example, we conduct the fitting problem and short-term prediction. Moreover, we compare the fitting effect and the prediction results of two models.

Forecasting stock market price by using fuzzified Choquet integral based fuzzy measures with genetic algorithm for parameter optimization

2020 ◽  
Vol 54 (2) ◽  
pp. 597-614
Author(s):  
Shanoli Samui Pal ◽  
Samarjit Kar
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Choquet Integral ◽  
Time Series Forecasting ◽  
Fuzzy Measure ◽  
Model Parameters ◽  
Forecasting Models ◽  
Non Linear

In this paper, fuzzified Choquet integral and fuzzy-valued integrand with respect to separate measures like fuzzy measure, signed fuzzy measure and intuitionistic fuzzy measure are used to develop regression model for forecasting. Fuzzified Choquet integral is used to build a regression model for forecasting time series with multiple attributes as predictor attributes. Linear regression based forecasting models are suffering from low accuracy and unable to approximate the non-linearity in time series. Whereas Choquet integral can be used as a general non-linear regression model with respect to non classical measures. In the Choquet integral based regression model parameters are optimized by using a real coded genetic algorithm (GA). In these forecasting models, fuzzified integrands denote the participation of an individual attribute or a group of attributes to predict the current situation. Here, more generalized Choquet integral, i.e., fuzzified Choquet integral is used in case of non-linear time series forecasting models. Three different real stock exchange data are used to predict the time series forecasting model. It is observed that the accuracy of prediction models highly depends on the non-linearity of the time series.

scholarly journals The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 526
Author(s):  
Xiaoe Ding ◽  
Minrui Zheng ◽  
Xinqi Zheng
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Search Algorithm ◽  
Sustainable Use ◽  
The United States ◽  
Future Research ◽  
Research Institution ◽  
Land Use Optimization ◽  
Optimization Stage ◽  
Optimization Search

Land use optimization (LUO) first considers which types of land use should exist in a certain area, and secondly, how to allocate these land use types to specific land grid units. As an intelligent global optimization search algorithm, the Genetic Algorithm (GA) has been widely used in this field. However, there are no comprehensive reviews concerning the development process for the application of the Genetic Algorithm in land use optimization (GA-LUO). This article used a bibliometric analysis method to explore current state and development trends for GA-LUO from 1154 relevant documents published over the past 25 years from Web of Science. We also displayed a visualization network from the aspects of core authors, research institutions, and highly cited literature. The results show the following: (1) The countries that published the most articles are the United States and China, and the Chinese Academy of Sciences is the research institution that publishes the most articles. (2) The top 10 cited articles focused on describing how to build GA models for multi-objective LUO. (3) According to the number of keywords that appear for the first time in each time period, we divided the process of GA-LUO into four stages: the presentation and improvement of methods stage (1995–2004), the optimization stage (2005–2008), the hybrid application of multiple models stage (2009–2016), and the introduction of the latest method stage (after 2017). Furthermore, future research trends are mainly manifested in integrating together algorithms with GA and deepening existing research results. This review could help researchers know this research domain well and provide effective solutions for land use problems to ensure the sustainable use of land resources.

QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR)

2013 ◽  
Vol 23 (5) ◽  
pp. 2264-2276 ◽  
Author(s):  
Eslam Pourbasheer ◽  
Reza Aalizadeh ◽  
Mohammad Reza Ganjali ◽  
Parviz Norouzi ◽  
Javad Shadmanesh ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Qsar Study ◽  
Multiple Linear Regression Method

scholarly journals Proving the Efficiency of Alternative Linear Regression Model Based on Mean Square Error (MSE) and Average Width using Aquaculture Data

2019 ◽  
Vol 8 (2S3) ◽  
pp. 377-381
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Mean Square Error ◽  
Linear Regression Model ◽  
Regression Method ◽  
Alternative Methods ◽  
Linear Regression Method ◽  
Mean Square ◽  
Average Width ◽  
Alternative Method

Multiple linear regressions (MLR) model is an important tool for investigating relationships between several response variables and some predictor variables. This method is very powerful and commonly used in finance, economic, medical, agriculture and many more. The main objective of this paper is to compare mean square error (MSE) and the average width between alternative linear regression models and linear regression model. The alternative method in this study is a combination of four methods, namely multiple linear regression method, the bootstrap method, a robust regression method and fuzzy regression through the construction of algorithms by using SAS software. Typically, the alternative method optimized by minimizing the mean square error (MSE) and average width. The results of the study showed a positive improvement for the estimation of parameters generated through these alternative methods

scholarly journals Pengaruh Modal Terhadap Pendapatan Pedagang (Studi kasus: Pasar Angso Duo Kota Jambi)

2020 ◽  
Vol 11 (2) ◽  
pp. 137
Author(s):  
Sudirman Sudirman ◽  
Ubaidillah Ubaidillah
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Method ◽  
Primary Data ◽  
Linear Regression Method ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Positive Effect ◽  
Market Trader

research aims to find out the effect of variable capital on the income of traders in Angso Duo Market Jambi City. The study used primary data by disseminating qusioners to 92 respondents. The method used in this study is a simple linear regression method. while econoetry analysis using a simple linear regression model is carried out to identify the variables that affect. Variables used are capital variables as free variables and income variables as bound variables. The results of the analysis showed that the capital had a positive effect on the earnings of Angso Duo Market traders. This means that if the market trader increases his capital, then the income of the market trader will also increase.

scholarly journals EVALUATION OF MULTIPLE LINEAR REGRESSION MODEL TO OBTAIN DBH OF TREES USING DATA FROM A LIGHTWEIGHT LASER SCANNING SYSTEM ON-BOARD A UAV

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 449-454 ◽  
Author(s):  
M. V. Machado ◽  
A. M. G. Tommaselli ◽  
V. M. Tachibana ◽  
R. P. Martins-Neto ◽  
M. B. Campos
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Statistical Tests ◽  
Low Cost ◽  
Multiple Linear Regression Model ◽  
Urban Trees ◽  
Scanning System ◽  
Linear Regression Method

<p><strong>Abstract.</strong> Vegetation mapping requires information about trees and underlying vegetation to ensure proper management of the urban and forest environments. This information can be obtained using remote sensors. For instance, lightweight systems composed of Unmanned Aerial Vehicles (UAVs) as a platform, low-cost laser units and the recent miniaturized navigation sensors (positioning and orientation) have become a very feasible and flexible alternative. Low-cost UAV-ALS systems usually provide centimetric accuracy in altimetry, according to flight data configuration and quality of observations. This paper presents a feasibility study of a lightweight ALS system on-board a UAV to estimate the diameters at breast height (DBH) of urban trees using LiDAR data and linear regression model. A mathematical model correlating the crown diameter and height of the tree to estimate the DBH was developed based on a linear regression with stepwise method. The stepwise linear regression method enables the addition and the removal of predictor variables through statistical tests. The tree samples were separated in two classes (A and B), according to the diametric distribution. These sample classes were used to define two linear regression models. The regression models that best fit the samples achieved an R<sup>2</sup> adj value above 94% for class A and B, which demonstrates the closeness between the samples and the developed mathematical models. The quality control of the proposed regression models was performed comparing the DBH values estimated and directly measured (reference). DBH of the trees were estimated with an average discrepancy of 8.7&amp;thinsp;cm.</p>

Study on State Identifying of Rotary Machine Torsional Oscillation Based on Wavelet Neural Network

2010 ◽  
Vol 26-28 ◽  
pp. 211-217
Author(s):  
Zong Meng ◽  
Feng Jie Fan ◽  
Bin Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Torsional Oscillation ◽  
Wavelet Neural Network ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Rotary Machine

This article established a new combining hierarchy genetic algorithm and multivariate linear regression model of WNN (wavelet neural network) for identify the feature of rotary machine. The effection on the question of nonlinear approximation is verified through the simulation and optimization. The test datas of a tandem mill are inputted into the model. After trained, the model has automatic ability of obtained the inspect information and the ability of adapt the changing of worked condition. The self-adaptive study and diagnosis of torsional oscillation state on different work condition are realized. The results verify the combining hierarchy genetic algorithm and multivariate linear regression model has the reliability.

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