Photo Layout with a Fast Evaluation Method and Genetic Algorithm

2012 ◽  
Author(s):  
Jian Fan
Keyword(s):  
Genetic Algorithm ◽  
Evaluation Method ◽  
Fast Evaluation

scholarly journals Color Image Evaluation for Small Space Based on FA and GEP

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Li Deng ◽  
Sui-Huai Yu ◽  
Wen-Jun Wang ◽  
Jun-Xuan Chen ◽  
Guo-Chang Liu
Keyword(s):  
Evaluation System ◽  
Frequency Conversion ◽  
Evaluation Method ◽  
Color Image ◽  
Gene Expression Programming ◽  
Image Evaluation ◽  
Evaluation Tool ◽  
Small Space ◽  
Entropy Weight ◽  
Fast Evaluation

Aiming at the problem that color image is difficult to quantify, this paper proposes an evaluation method of color image for small space based on factor analysis (FA) and gene expression programming (GEP) and constructs a correlation model between color image factors and comprehensive color image. The basic color samples of small space and color images are evaluated by semantic differential method (SD method), color image factors are selected via dimension reduction in FA, factor score function is established, and by combining the entropy weight method to determine each factor weights then the comprehensive color image score is calculated finally. The best fitting function between color image factors and comprehensive color image is obtained by GEP algorithm, which can predict the users’ color image values. A color image evaluation system for small space is developed based on this model. The color evaluation of a control room on AC frequency conversion rig is taken as an example, verifying the effectiveness of the proposed method. It also can assist the designers in other color designs and provide a fast evaluation tool for testing users’ color image.

Research and Evaluation of Bionics Shape Design Based on Cases

2014 ◽  
Vol 551 ◽  
pp. 621-625
Author(s):  
Nan Chu Guo
Keyword(s):  
Genetic Algorithm ◽  
Quantitative Methods ◽  
Evaluation Method ◽  
Evaluation Methods ◽  
Shape Design ◽  
Fuzzy Evaluation ◽  
Ideal Approach ◽  
Comprehensive Fuzzy Evaluation

The paper proposes an ideal approach of shape design by using shape evaluation methods accurately. The paper proposes and tests the comprehensive fuzzy evaluation method using a case of two clips based on genetic algorithm and quantitative methods. By using this evaluation method, the shape details of a product could be improved gradually.

Reliability of nonrepairable phased-mission systems with common bus performance sharing

2018 ◽  
Vol 232 (6) ◽  
pp. 647-660 ◽  
Author(s):  
Huan Yu ◽  
Jun Yang ◽  
Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Reliability Analysis ◽  
Evaluation Method ◽  
Optimal Allocation ◽  
Recursive Algorithm ◽  
Entire System ◽  
Allocation Algorithm ◽  
Proposed Model ◽  
The Common ◽  
Multi Phase

This article considers the reliability analysis of phased-mission systems with common bus performance sharing. The whole system consists of client nodes, service elements, and a common bus redistribution system and it undertakes a multi-phase mission. In each phase, the service elements must satisfy the demands of the prespecified client nodes set. The service elements can share their surplus performance with other client nodes through the common bus. In any phase, the system fails if the demands of the prespecified client nodes set are not satisfied. In other words, the entire system succeeds if the demands of the prespecified client nodes set are satisfied in all phases. The reliability of the proposed model is analyzed by the backward recursive algorithm. The optimal allocation problem is solved by the genetic algorithm. Two examples are presented to demonstrate the proposed reliability evaluation method and optimal allocation algorithm.

Genetic multistart algorithm for the design of fault-tolerant systems

2008 ◽  
Vol 222 (1) ◽  
pp. 17-29
Author(s):  
K Echtle ◽  
I Eusgeld ◽  
D Hirsch
Keyword(s):  
Genetic Algorithm ◽  
Fault Tolerance ◽  
Fault Tolerant ◽  
Design Method ◽  
Fitness Function ◽  
Evolutionary Process ◽  
Mechatronic Systems ◽  
Fast Evaluation ◽  
Pareto Solution ◽  
Fault Tolerant Systems

This paper presents a new approach to the multiobjective design of fault-tolerant systems. The design objectives are fault tolerance and cost. Reducing the cost is of particular importance for fault-tolerant systems because the overhead caused by redundant components is considerable. The new design method consists of a special genetic algorithm that is tailored to the particular issues of fault-tolerant systems. The interface of the present tool ePADuGA (elitist and Pareto-based Approach to Design fault-tolerant systems using a Genetic Algorithm) allows for adaptation to various fields of application. The degree of fault tolerance is measured by the number of tolerated faults rather than traditional reliability metrics, because reliability numbers are mostly unknown during early design phases. The special features of the genetic algorithm comprise a graph-oriented representation of systems (which are the individuals during the evolutionary process), a simple yet expressive fault model, a very efficient procedure for fault-tolerance evaluation, and a Pareto-oriented fitness function. In a genetic algorithm generating thousands of individuals, a very fast evaluation of each individual is mandatory. For this purpose, state-space-oriented evaluation methods have been cut down to an extremely simple function which is still sufficient to assess the fault tolerance of individuals. An innovative aspect is also a multistart technique to find a Pareto solution set, which is independent of any parameters. In this paper, experimental results are presented showing the feasibility of the approach as well as the usefulness of the final fault-tolerant architectures, particularly in the field of mechatronic systems.

scholarly journals A Method for Fast Evaluation of Potential Consequences of Dam Breach

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2224 ◽  
Author(s):  
Wei Ge ◽  
Yutie Jiao ◽  
Heqiang Sun ◽  
Zongkun Li ◽  
Hexiang Zhang ◽  
...  
Keyword(s):  
At Risk ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Physical Models ◽  
Evaluation Index System ◽  
Building Vulnerability ◽  
Dam Breach ◽  
Fast Analysis ◽  
Fast Evaluation ◽  
Loss Of Life

Dam breach has catastrophic consequences for human lives and economy. In previous studies, empirical models are often, to a limited extent, due to the inadequacy of historical dam breach events. Physical models, which focus on simulating human behavior during floods, are not suitable for fast analysis of a large number of dams due to the complexities of many key parameters. Therefore, this paper proposes a method for fast evaluation of potential consequences of dam breach. Eight main indices, i.e., capacity of reservoir (CR), dam height (HD), population at risk (PR), economy at risk (ER), understanding of dam breach (UB), industry type (TI), warning time (TW), and building vulnerability (VB), are selected to establish an evaluation index system. A catastrophe evaluation method is introduced to establish an evaluation model for potential consequences of dam breach based on the indices which are divided into five grades according to the relevant standards and guidelines. Validation of the method by twelve historical dam breach events shows a good accuracy. The method is applied to evaluate potential consequences of dam breach of Jiangang Reservoir in Henan Province, China. It is estimated that loss of life in the worst scenario is between that of Hengjiang Reservoir and that of Shimantan Reservoir dam breach, of which fatalities are 941 and 2717, respectively, showing that risk management measures should be taken to reduce the risk of potential loss of life.

Power Information System Risk Assessment Method Based on Genetic Algorithms and Neural Network

2014 ◽  
Vol 530-531 ◽  
pp. 429-433 ◽  
Author(s):  
Heng Yang ◽  
Ru Sen Fan ◽  
Dong Hui Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Risk Assessment ◽  
Information System ◽  
Evaluation Method ◽  
Assessment Method ◽  
Local Extremum ◽  
Bp Algorithm ◽  
Bp Network ◽  
System Risk

In order to scientifically and accurately evaluate power information system, the new power information risk evaluation method based on the genetic algorithm and BP neural network is presented. The method combining the genetic algorithm and BP algorithm can be used to train the feedforward neural network , namely, first , to use the genetic algorithm to do the global training, then ,to use BP algorithm to do local precise training ,which not only overcomes the drawbacks of the traditional BP network (the training time is long, and the network is easy to fall to local extremum),but also improves the global convergence efficiency. The method was adopted to evaluate the power information system. And findings identify that the new method has distinctive convergence speed and high predicition accuracy, which provides a new concept for power information system risk assessment.

scholarly journals Quality Evaluation Method of a Mathematics Teaching Model Reform Based on an Improved Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yun Yang
Keyword(s):  
Genetic Algorithm ◽  
Mathematics Teaching ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Teaching Quality ◽  
Teaching Model ◽  
College Mathematics ◽  
Improved Genetic Algorithm ◽  
Training Process ◽  
Convolutional Network

The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the traditional college mathematics teaching model reform results in low accuracy of the evaluation outcomes. In this paper, aiming at this problem, a quality evaluation method for the college mathematics teaching model reform, based on the genetic algorithm, is proposed. The simulated annealing algorithm uses the weighted comprehensive objective evaluation multiobjective optimization effect that can effectively improve the accuracy of the evaluation results. In the training process, the gradient descent back-propagation training method is used to obtain new weights for the quality evaluation of college mathematics teaching mode reforms and to score various indicators and evaluate the indicators. The mean value of the outcomes is the result of mathematics teaching quality evaluation. The experimental results show that the training error of the convolutional network of the proposed method is significantly small. Based on the genetic algorithm that improves the convolutional network training process, the obtained quality evaluation outcomes are higher in accuracy, better in goodness of fitness function, and considerably lower than other state-of-the-art methods. We observed that the improved genetic algorithm has a more than 90% goodness of fit and the error is significantly lower, that is, 0.01 to 0.04, than the classical genetic algorithm.

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