scholarly journals Optimization of Zoom Lens with Discrete State of Liquid Lens Elements by Using Genetic Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Cheng-Mu Tsai
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Spot Size ◽  
Zoom Lens ◽  
Discrete Variation ◽  
Voltage Level ◽  
Discrete State ◽  
Lens Element ◽  
Liquid Lens ◽  
Crossover And Mutation

This paper is to employ liquid lens elements to design a lens with zoom function by using the genetic algorithm (GA) optimization. The liquid lens elements used in the proposal can apply voltage adjustment to generate the electrical field that induces the liquid with electric conductivity to vary the surface curvature between two different kinds of liquids. According to the voltage level, the liquid lens element makes the discrete variation of the curvature and thickness realize the zoom function without moving the lens groups so that the overall length can be reduced. However, it is difficult to design the zoom lens under the discrete variation of the curvature and thickness in the liquid lens elements and the mechanical space that is constantly limited. The GA offers a flexible way for lens optimization. We regarded the spot size as the fitness function to look for the optimum curvatures, thickness, and the corresponding statuses of liquid lens elements for the zoom lens. As a result, the zoom lens with constant space can be realized by running the selection, crossover, and mutation operation in the GA optimization.

Test Methods Research of Cracks in Ceramics Based on Genetic Algorithm

2014 ◽  
Vol 977 ◽  
pp. 25-29
Author(s):  
Bing Xiang Liu ◽  
Feng Qin Wang ◽  
Xu Dong Wu ◽  
Ying Xi Li
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Optimization Problem ◽  
Fitness Function ◽  
Recognition Rate ◽  
Test Methods ◽  
Segmentation Method ◽  
Maximum Variance ◽  
Best Value ◽  
Crossover And Mutation

In order to improve the reliability of cracks in ceramics test, this paper puts forward a target adaptive segmentation method used by genetic algorithm and maximum-variance algorithm in all classes. This proposed method makes some appropriate improvements about crossover and mutation in genetic algorithm. Besides, the fitness function draws merits of maximum-variance algorithm in all classes and turns the best value in image segmentation into corresponding optimization problem. The simulation results of experiment shows the method proposed shortens the searching time and strengthens anti-noise property during image segmentation and improves recognition rate of cracks in ceramics.

Application of Intelligent Algorithm to Transformer Optimal Design

2011 ◽  
Vol 219-220 ◽  
pp. 1578-1583
Author(s):  
Shuang Zhang ◽  
Qing He Hu ◽  
Xing Wei Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Penalty Function ◽  
Fitness Function ◽  
Mutation Rates ◽  
Adaptive Genetic Algorithm ◽  
Speed Of Convergence ◽  
Intelligent Algorithm ◽  
Transformer Model ◽  
Crossover And Mutation

The paper studies transformer optimal design, establishes optimal transformer model based on total owning cost. It adopts penalty function to process objective function with weighted coefficients. For prematurity and low speed of convergence of Simple Genetic Algorithm, improved adaptive genetic algorithm is adopted. It increases crossover and mutation rates, and improves fitness function. It is adopted to search for minimum total owning cost of transformer. The result shows that the algorithm performs well, increases converging speed and betters solution.

Assembly Sequence Generation and Optimization for Compliant Assemblies Based on Genetic Algorithm

2011 ◽  
Vol 230-232 ◽  
pp. 978-981
Author(s):  
Yan Feng Xing ◽  
Yan Song Wang ◽  
Xiao Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Initial Population ◽  
Assembly Sequence ◽  
Constraint Function ◽  
Assembly Modeling ◽  
Sequence Generation ◽  
Assembly Sequences ◽  
Assembly Sequence Generation ◽  
Crossover And Mutation

This paper proposes a genetic algorithm to generate and optimize assembly sequences for compliant assemblies. An assembly modeling is presented to describe the geometry of the assembly, which includes three sets of parts, relationships and joints among the parts. Based on the assembly modeling, an assembly sequence is denoted as an individual, which is assigned an evaluation function that consists of the fitness and constraint functions. The fitness function is used to evaluate feasible sequences; in addition, the constraint function is employed to evolve unfeasible sequences. The genetic algorithm starts with a randomly initial population of chromosomes, evolves new populations by using reproduction, crossover and mutation operations, and terminates until acceptable sequences output. Finally an auto-body side assembly is used to illustrate the algorithm of assembly sequence generation and optimization.

scholarly journals Method of Association Rules Mining and Its Application in Analysis of Seawater Samples

2018 ◽  
Vol 14 (05) ◽  
pp. 172
Author(s):  
Qiuhong Sun ◽  
Xinhang Xu ◽  
Yonghong Liu ◽  
Hongtao Zhang
Keyword(s):  
Genetic Algorithm ◽  
Association Rules ◽  
Fitness Function ◽  
Actual Case ◽  
Association Rules Mining ◽  
Research Findings ◽  
Seawater Quality ◽  
Seawater Samples ◽  
Set Up ◽  
Crossover And Mutation

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">This paper aims to set up new rules for processing seawater quality monitoring data collected by photoelectric sensor network, and mine out the useful information contained in the data. For this purpose, the immune algorithm was introduced to the classical genetic algorithm, the fitness function was designed, and the crossover and mutation probabilities were adjusted, thus creating the adaptive immune genetic algorithm (IIGA). The new algorithm was described in details and applied in an actual case. Through the comparison between the IIGA, IGA and apriori algorithms, the author concluded that the IIGA not only shortened the mining time, but also ensured the operation accuracy. The research findings are of great importance to the association rules mining in various fields.</span>

A New Flatness Evaluation-Rotation Method Based on GA

2010 ◽  
Vol 139-141 ◽  
pp. 2033-2037 ◽  
Author(s):  
Yan Ming Jiang ◽  
Gui Xiong Liu
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Three Dimensions ◽  
Local Optimum ◽  
Mutation Probability ◽  
Projection Length ◽  
Rotation Method ◽  
Minimum Zone ◽  
Crossover And Mutation

Flatness is one fundamental element of geometric forms, and the flatness evaluation is particularly important for ensuring the quality of industrial products. This paper presents a new flatness evaluation in the view of the minimum zone evaluation - rotation method based on genetic algorithm. This method determines the minimum zone through rotating measurement points in three dimensions coordinate. The points are firstly rotated about coordinate axes. Then they are projected in one axis, and the smallest projection length is the flatness value. The rotation angles are optimized by genetic algorithm to improve search efficiency. An exponential fitness function and the rotation angles range is designed on the basis of flatness characteristics. An adaptive mode of crossover and mutation probability is used to avoid local optimum. The results show this method can search the minimum zone and converge rapidly.

Variable Selection in Multiple Linear Regression Using a Genetic Algorithm

2016 ◽  
pp. 133-159 ◽  
Author(s):  
Javier Trejos ◽  
Mario A. Villalobos-Arias ◽  
Jose Luis Espinoza
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Fitness Function ◽  
Data Sets ◽  
Genetic Operators ◽  
Wheel Model ◽  
Crossover And Mutation

In this article it is studied the application of a genetic algorithm in the problem of variable selection for multiple linear regression, minimizing the least squares criterion. The algorithm is based on a chromosomic representation of variables that are considered in the least squares model. A binary chromosome indicates the presence (1) or absence (0) of a variable in the model. The fitness function is based on the adjusted square R, proportional to the fitness for chromosome selection in a roulette wheel model selection. Usual genetic operators, such as crossover and mutation are implemented. Comparisons are performed with benchmark data sets, obtaining satisfying and promising results.

The Strong Prime Numbers Generation Algorithm Based on Genetic Algorithm

2012 ◽  
Vol 220-223 ◽  
pp. 2963-2967
Author(s):  
Zhi Hua Hu ◽  
Qing Zhang
Keyword(s):  
Genetic Algorithm ◽  
Prime Number ◽  
Fitness Function ◽  
Prime Numbers ◽  
Modular Exponentiation ◽  
Generation Algorithm ◽  
Rsa Algorithm ◽  
Mutation Strategies ◽  
Crossover And Mutation ◽  
Computing Speed

Large prime number generation methods in need of a more complex modular exponentiation, leading to defects of slower computing speed, Based on this genetic algorithm, proposed a new strong prime number generated algorithm. The method according to the characteristics of Strong Primes, the algorithm is simple, easy to implement to meet the needs of the RSA algorithm security , giving the method of determining the large prime numbers. Design fitness function , crossover and mutation strategies which can be used in genetic algorithm. Finally design the algorithm of producing Strong prime numbers

Optimization of Different Fitness Functions Using Reproduction, Crossover and Mutation of Genetic Algorithm With Binary Number Scheduling

2021 ◽  
Author(s):  
J PRINCE JEROME CHRISTOPHER ◽  
K LINGADURAI ◽  
G SHANKAR
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Number System ◽  
Binary Number ◽  
Genetic Operators ◽  
Fitness Functions ◽  
Decimal Number ◽  
Novel Approach ◽  
Operator Mutation ◽  
Crossover And Mutation

Abstract Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics. In this paper, we investigate a novel approach to the binary coded testing process based on a genetic algorithm. This paper consists of two parts. Thefirst part addresses the problem in the traditional way of using the decimal number system to define the fitness function to study the variations of counts and the variations of probability against the fitness functions. Second, the initialpopulationsare defined using binary coded digits (genes). For the evaluation of the high fitness function values,three genetic operators, namely, reproduction, crossover and mutation, are randomly used. The results show the importance of the genetic operator, mutation, which yields the peak values for the fitness function based on binary coded numbers performed in a new way.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

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