Niche Genetic Algorithm for Solving Multiplicity Problems in Genetic Association Studies

Chou, Fu-I; Ho, Wen-Hsien; Chen, Chiu-Hung

doi:10.31209/2019.100000144

Cited by 3 publications

(3 citation statements)

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“…The niche genetic algorithm is to preserve the population, reduce the fitness of the population, increase the probability of elimination, and increase the diversity of the population. It is a more accurate model [3]. The research is using the genetic algorithm to optimize the model constructed by the neural network, which is more accurate and scientific for prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Kuang

2022

Journal of Function Spaces

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With the continuous development of urbanization, the urban population is becoming more and more dense, and the demand for land is becoming more and more tense. Urban expansion has become an indispensable part of urban development. This paper studies the optimization of neural network structure by genetic algorithm, puts forward the prediction model of urban scale expansion based on a genetic algorithm optimization neural network, and compares the performance of the model with the basic model. A genetic algorithm BP neural network (GA-BP) optimized by the genetic algorithm is used to shorten the running time of the algorithm and improve the prediction accuracy, but it is easy to fall into local solution. The genetic algorithm is improved by immune cloning algorithm, and the CGA-BP neural network model is established to obtain the global optimal solution. Compared with the BP neural network model and GA-BP neural network model, the CGA-BP neural network model converges faster, and the training times reach the error condition after 79 times, while the BP neural network model and GA-BP neural network model need 117 times and 100 times, respectively, and the fitness value corresponding to the number of iterations of the model is larger. Therefore, the CGA-BP neural network algorithm can make prediction more accurately and quickly and predict the expansion of urban scale through urban conditions.

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Section: Introductionmentioning

confidence: 99%

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Kuang

2022

Journal of Function Spaces

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“…The Pareto optimal solution set of this kind of integrated optimisation problem can be obtained by optimising the control strategy parameters with both consumption and emission as the optimisation objectives. This solution can provide various options for setting control strategy parameters [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Model of a Composite Energy Storage System for Urban Rail Trains

Liang¹,

Qi²,

Liang³

2022

Computer Systems Science and Engineering

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Urban rail transit can solve the current inconvenient transportation problem for China's large urban population. A compound onboard energy storage system can meet vehicles' traction requirements and recover energy in vehicles' braking stage to improve energy utilisation. However, the composite onboard energy storage system has several concerns, such as its power and energy demand, battery aging, and maintenance costs. Therefore, the NSGA-II algorithm is proposed to optimise matching the composite energy storage system parameters for urban rail trains. The NSGA-II algorithm was used with an improved elite retention strategy to optimise the parameters matching of the composite power supply. The optimisation's objective concerns the replacement costs and economy of the composite power supply. The method increases the vertical diversity of searching and avoids genetic precocity. The NSGA-II algorithm calls the simulation model of composite power supply in real-time and simultaneously optimises the composite power supply and control parameters. The Pareto set of optimisation objectives and corresponding parameters and control strategies of composite power supply are obtained. The NSGA-II algorithm can optimise the composite energy storage system's parameters and improve the train and the composite power supply's performance indexes. The algorithm greatly reduces the composite power supply's capacitance and reduces the system's total energy consumption. Then, the multi-component energy loss caused by the multi-power source system can be effectively controlled. The total capacitance is reduced by 12.1%, the battery life is prolonged by 18.86%, and the optimised composite power supply's energy storage is increased by 17.6%.

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“…( [18][19][20] is to simulate the evolutionary process of the population, which is to conduct organized random information exchange and recombination for individuals [21,22]. In the string structure of the previous generation, adaptive bits and segments are selected to recombine to generate a new generation of population, namely, "survival of the fittest.…”

Section: Introductionmentioning

confidence: 99%

An Improved Genetic Algorithm and Neural Network‐Based Evaluation Model of Classroom Teaching Quality in Colleges and Universities

Zhang¹,

Xiao²,

Li³

et al. 2021

Wireless Communications and Mobile Computing

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Research on educational quality has gotten a lot of attention as the current higher education teaching reform continues to deepen and grow. The key to improving education quality is to improve teaching quality, and teacher evaluation is an important tool for doing so. As a result, educational management requires the development and refinement of a system for evaluating teaching quality. Traditional approaches to assessing teaching quality, on the other hand, are problematic due to their limitations. As a result, a scientific and reasonable model for evaluating the teaching quality of college undergraduate teachers must be developed. We present a unique model for evaluating the quality of classroom teaching in colleges and universities, which is based on improved genetic algorithms and neural networks. The basic idea is to use adaptive mutation genetic algorithms to refine the initial weights and thresholds of the BP neural network. The teaching quality evaluation findings were improved by improving the neural network’s prediction accuracy and convergence speed, resulting in a more practical scheme for evaluating college and university teaching quality. We have conducted simulation experiments and comparative analysis, and the mean square error of the results of the proposed model is very low, which proves the effectiveness and superiority of the algorithm.

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Niche Genetic Algorithm for Solving Multiplicity Problems in Genetic Association Studies

Cited by 3 publications

References 0 publications

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Model of a Composite Energy Storage System for Urban Rail Trains

An Improved Genetic Algorithm and Neural Network‐Based Evaluation Model of Classroom Teaching Quality in Colleges and Universities

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