A Bi-objective Genetic Algorithm Optimization of Chaos-DNA Based Hybrid Approach

2019 ◽  
Vol 28 (2) ◽  
pp. 333-346 ◽  
Author(s):  
Shelza Suri ◽  
Ritu Vijay
Keyword(s):  
Genetic Algorithm ◽  
Image Encryption ◽  
Fitness Function ◽  
Hybrid Approach ◽  
Good Choice ◽  
Algorithm Optimization ◽  
Change Rate ◽  
Fitness Functions ◽  
Significant Performance ◽  
The Given

Abstract The paper implements and optimizes the performance of a currently proposed chaos-deoxyribonucleic acid (DNA)-based hybrid approach to encrypt images using a bi-objective genetic algorithm (GA) optimization. Image encryption is a multi-objective problem. Optimizing the same using one fitness function may not be a good choice, as it can result in different outcomes concerning other fitness functions. The proposed work initially encrypts the given image using chaotic function and DNA masks. Further, GA uses two fitness functions – entropy with correlation coefficient (CC), entropy with unified average changing intensity (UACI), and entropy with number of pixel change rate (NPCR) – simultaneously to optimize the encrypted data in the second stage. The bi-objective optimization using entropy with CC shows significant performance gain over the single-objective GA optimization for image encryption.

A Multiple Objective Fitness Function Design Scheme for GA-Based Cognitive Engines

2015 ◽  
Vol 764-765 ◽  
pp. 444-447
Author(s):  
Keun Hong Chae ◽  
Hua Ping Liu ◽  
Seok Ho Yoon
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Fitness Function ◽  
Multiple Objective ◽  
Cognitive Engine ◽  
Fitness Functions ◽  
Design Scheme ◽  
Transmission Parameter ◽  
Function Design ◽  
Single Objective

In this paper, we propose a multiple objective fitness function for cognitive engines by using the genetic algorithm (GA). Specifically, we propose four single objective fitness functions, and finally, we propose a multiple objective fitness function based on the single objective fitness functions for transmission parameter optimization. Numerical results demonstrate that we can obtain transmission parameter sets optimized for given transmission scenarios with the GA-based cognitive engine incorporating the proposed objective fitness function.

Development of Control Signatures with a Hybrid Data Mining and Genetic Algorithm

2008 ◽  
pp. 2226-2247
Author(s):  
Alex Burns ◽  
Shital Shah ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Power Plant ◽  
Hybrid Method ◽  
Fitness Function ◽  
Hybrid Approach ◽  
Power Plant Boiler ◽  
Best Parameter ◽  
Feasible Solutions ◽  
Plant Boiler

This paper presents a hybrid approach that integrates a genetic algorithm (GA) and data mining to produce control signatures. The control signatures define the best parameter intervals leading to a desired outcome. This hybrid method integrates multiple rule sets generated by a data mining algorithm with the fitness function of a GA. The solutions of the GA represent intersections among rules providing tight parameter bounds. The integration of intuitive rules provides an explanation for each generated control setting and it provides insights into the decision making process. The ability to analyze parameter trends and the feasible solutions generated by the GA with respect to the outcomes is another benefit of the proposed hybrid method. The presented approach for deriving control signatures is applicable to various domains, such as energy, medical protocols, manufacturing, airline operations, customer service, and so on. Control signatures were developed and tested for control of a power plant boiler. These signatures discovered insightful relationships among parameters. The results and benefits of the proposed method for the power plant boiler are discussed in the paper.

Development of Control Signatures with a Hybrid Data Mining and Genetic Algorithm

2011 ◽  
pp. 179-203
Author(s):  
Alex Burns ◽  
Shital Shah ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Power Plant ◽  
Hybrid Method ◽  
Fitness Function ◽  
Hybrid Approach ◽  
Power Plant Boiler ◽  
Best Parameter ◽  
Feasible Solutions ◽  
Plant Boiler

This paper presents a hybrid approach that integrates a genetic algorithm (GA) and data mining to produce control signatures. The control signatures define the best parameter intervals leading to a desired outcome. This hybrid method integrates multiple rule sets generated by a data mining algorithm with the fitness function of a GA. The solutions of the GA represent intersections among rules providing tight parameter bounds. The integration of intuitive rules provides an explanation for each generated control setting and it provides insights into the decision making process. The ability to analyze parameter trends and the feasible solutions generated by the GA with respect to the outcomes is another benefit of the proposed hybrid method. The presented approach for deriving control signatures is applicable to various domains, such as energy, medical protocols, manufacturing, airline operations, customer service, and so on. Control signatures were developed and tested for control of a power plant boiler. These signatures discovered insightful relationships among parameters. The results and benefits of the proposed method for the power plant boiler are discussed in the paper.

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.

A Template Matching Method Based on Genetic Algorithm Optimization

2012 ◽  
Vol 220-223 ◽  
pp. 1298-1302 ◽  
Author(s):  
Xiao Hui Zhang ◽  
Qing Liu ◽  
Mu Li
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Template Matching ◽  
Fitness Function ◽  
Image Process ◽  
Algorithm Optimization ◽  
Matching Method ◽  
Correlation Degree ◽  
Degree Function ◽  
Effective Performance

This paper presents a method of using Genetic Algorithm (GA) to optimize template and image searching process, using template matching to recognize target. An initial matching template is set manually according to 2D shape and the optimizing template is obtained by GA optimizing to meet the requirement of real-time and effective performance. Then the pixel position is encoded into genes, template correlation degree function works as fitness function to do GA search to recognize the target. The relating image process experiments show that this method has good real-time and robustness performance.

scholarly journals Hardware Genetic Algorithm Optimization by Critical Path Analysis using a Custom VLSI Architecture

2015 ◽  
Vol 56 ◽  
Author(s):  
Farouk Smith ◽  
Allan Edward Van den Berg
Keyword(s):  
Genetic Algorithm ◽  
Critical Path ◽  
Fitness Function ◽  
Vlsi Architecture ◽  
Digital Circuit ◽  
Direct Access ◽  
Algorithm Optimization ◽  
Modular Evolution ◽  
Depth Analysis ◽  
Evolutionary Hardware

This paper propose a Virtual-Field Programmable Gate Array (V-FPGA) architecture that allows direct access to its configuration bits to facilitate hardware evolution, thereby allowing any combinational or sequential digital circuit to be realized. By using the V-FPGA, this paper investigates two possible ways of making evolutionary hardware systems more scalable: by optimizing the system’s genetic algorithm (GA); and by decomposing the solution circuit into smaller, evolvable sub-circuits. GA optimization is done by: omitting a canonical GA’s crossover operator (i.e. by using a 1+λ algorithm); applying evolution constraints; and optimizing the fitness function. A noteworthy contribution this research has made is the in-depth analysis of the phenotypes’ CPs. Through analyzing the CPs, it has been shown that a great amount of insight can be gained into a phenotype’s fitness. We found that as the number of columns in the Cartesian Genetic Programming array increases, so the likelihood of an external output being placed in the column decreases. Furthermore, the number of used LEs per column also substantially decreases per added column. Finally, we demonstrated the evolution of a state-decomposed control circuit. It was shown that the evolution of each state’s sub-circuit was possible, and suggest that modular evolution can be a successful tool when dealing with scalability.

scholarly journals Reduction dimensionality of hyperspectral imagery using genetic algorithm and mutual information and normalized mutual information as a fitness function

2021 ◽  
Author(s):  
Merzouqi Maria ◽  
Sarhrouni El Kebir ◽  
Hammouch Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Mutual Information ◽  
Fitness Function ◽  
Pearson Correlation ◽  
Hyperspectral Images ◽  
Normalized Mutual Information ◽  
Bhattacharyya Coefficient ◽  
Additional Information ◽  
Fitness Functions ◽  
Novel Method

AbstractHyperspectral images (HSI) present a wealth of information. It is distinguished by its high dimensionality. It served humanity in many fields. The quantity of HSI information represents a double-edged sword. As a consequence, their dimensionality must be reduced. Nowadays, several methods are proposed to overcome their duress. The most useful and essential solution is selection approaches of hyperspectral bands to analyze it quickly. Our work suggests a novel method to achieve this selection: we introduce a Genetic Algorithm (GA) based on mutual information (MI) and Normalized Mutual Information (NMI) as fitness functions. It selects the relevant bands from noisiest and redundant ones that don’t contain any additional information. .The proposed method is applied to three different HSI: INDIAN PINE, PAVIA, and SALINAS. The introduced algorithm provides a remarkable efficiency on the accuracy of the classification, in front of other statistical methods: the Bhattacharyya coefficient as well as the inter-bands correlation (Pearson correlation). We conclude that the measure of information (MI, NMI) provides more efficiency as a fitness function for GA selection applied to HSI; it must be more investigated.

Design of Fractional Order PID Controller Using Genetic Algorithm Optimization Technique for Nonlinear System

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Shivanky Jaiswal ◽  
Chiluka Suresh Kumar ◽  
Murali Mohan Seepana ◽  
G. Uday Bhaskar Babu
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear System ◽  
Fractional Order ◽  
Pid Controller ◽  
Fitness Function ◽  
Optimization Technique ◽  
Algorithm Optimization ◽  
Simulation Results ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

AbstractIn this paper, fractional order PID controller, as well as integer order PID controller, is designed for non-linear system to enhance the system’s performance and gain the stability. The novelty of the work is achieved by the development of a new methodology for integer order PID and fractional order PID control tuning by optimizing the parameters of controllers using the Genetic Algorithms optimization technique. The performance of any system mainly depends upon how efficiently the controller will be working and hence that’s how most crucial part of the designing of FOPID controller or any controller is the tuning of its parameters. The uniquely designed and tuned parameters of the FOPID controller which is obtained by optimizing all the five parameters by using an evolutionary algorithm optimization technique i. e. a genetic algorithm which is a very powerful search tool and carrying heuristic characteristics. This method of tuning the FOPID controller which is designed and has been applied over the conical tank (nonlinear) system. The most important step in applying genetic algorithm is the selection of the fitness function and hence Integral of time multiplied by absolute error (ITAE) have been used here as the fitness function. Each chromosome comprised of all the five parameters of FOPID controller, which have been further optimised using above mentioned fitness function. From the simulation results, it can be observed that the solutions which are obtained optimally, presents an excellent performance for the system studied, by improving the behaviour of the system satisfactorily. Simulation results also show that the proposed FOPID controller gives improved performance over classical PID controller in terms of IAE and TV.

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