scholarly journals OBJECTS IMAGES ALIGNMENT WITH THE USE OF GENETIC AND GRADIENT ALGORITHMS

2014 ◽  
pp. 160-169
Author(s):  
Sergiy Balovsyak ◽  
Igor Fodchuk
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Hybrid Method ◽  
Digital Images ◽  
Image Alignment ◽  
Real Numbers ◽  
Accuracy Improvement ◽  
Gradient Algorithms ◽  
Tournament Selection ◽  
The Given

The given paper presents a hybrid method which is a combination of genetic and gradient algorithms used for the comparison of digital images of an object. Aligning the images, their basic transformations are taken into account, namely shift and scale in a width and height, angle, changes in intensity and contrast. The software for image alignment of objects has been created using Delphi environment. The program utilizes modified genetic algorithms where the chromosomes are the vectors of real numbers. The methods of roulette, rank and tournament selection are used for chromosome selection. After the use of the genetic algorithm the object images were compared by the method of coordinate descent that provides an accuracy improvement of image alignment. The efficiency of different methods of chromosome selection in the genetic algorithm for images alignment is researched. The size of chromosome population as well as other parameters of genetic algorithm have been optimized.

Innovative Genetic Algorithmic Approach to Select Potential Patches Enclosing Real and Complex Zeros of Nonlinear Equation

2017 ◽  
Vol 6 (2) ◽  
pp. 18-37 ◽  
Author(s):  
Vijaya Lakshmi V. Nadimpalli ◽  
Rajeev Wankar ◽  
Raghavendra Rao Chillarige
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equation ◽  
Complex Plane ◽  
Search Space ◽  
Regions Of Interest ◽  
Real Numbers ◽  
Algorithmic Approach ◽  
Space Reduction ◽  
Complex Zeros ◽  
The Given

In this article, an innovative Genetic Algorithm is proposed to find potential patches enclosing roots of real valued function f:R→R. As roots of f can be real as well as complex, the function is reframed on to complex plane by writing it as f(z). Thus, the problem now is transformed to finding potential patches (rectangles in C) enclosing z such that f(z)=0, which is resolved into two components as real and imaginary parts. The proposed GA generates two random populations of real numbers for the real and imaginary parts in the given regions of interest and no other initial guesses are needed. This is the prominent advantage of the method in contrast to various other methods. Additionally, the proposed ‘Refinement technique' aids in the exhaustive coverage of potential patches enclosing roots and reinforces the selected potential rectangles to be narrow, resulting in significant search space reduction. The method works efficiently even when the roots are closely packed. A set of benchmark functions are presented and the results show the effectiveness and robustness of the new method.

scholarly journals Hybrid Modeling KMeans – Genetic Algorithms in the Health Care Data

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Tessy Badriyah
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Health Care ◽  
Genetic Algorithms ◽  
Hybrid Method ◽  
Health Care Data ◽  
Computational Performance ◽  
Genetic Algorithm Approach ◽  
A New Technique ◽  
Published Research

K-Means is one of the major algorithms widely used in clustering due to its good computational performance. However, K-Means is very sensitive to the initially selected points which randomly selected, and therefore it does not always generate optimum solutions. Genetic algorithm approach can be applied to solve this problem. In this research we examine the potential of applying hybrid GA- KMeans with focus on the area of health care data. We proposed a new technique using hybrid method combining KMeans Clustering and Genetic Algorithms, called the “Hybrid K-Means Genetic Algorithms” (HKGA). HKGA combines the power of Genetic Algorithms and the efficiency of K-Means Clustering. We compare our results with other conventional algorithms and also with other published research as well. Our results demonstrate that the HKGA achieves very good results and in some cases superior to other methods.Keywords: Machine Learning, K-Means, Genetic Algorithms, Hybrid KMeans Genetic Algorithm (HGKA).

scholarly journals Applicability of Cellular Automata in Cryptanalysis

Cryptography ◽  
2020 ◽  
pp. 180-191
Author(s):  
Harsh Bhasin ◽  
Naved Alam
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cellular Automata ◽  
Literature Review ◽  
Soft Computing ◽  
Search Problem ◽  
Extensive Literature ◽  
Soft Computing Techniques ◽  
A New Technique ◽  
The Given

Cryptanalysis refers to finding the plaintext from the given cipher text. The problem reduces to finding the correct key from a set of possible keys, which is basically a search problem. Many researchers have put in a lot of effort to accomplish this task. Most of the efforts used conventional techniques. However, soft computing techniques like Genetic Algorithms are generally good in optimized search, though the applicability of such techniques to cryptanalysis is still a contentious point. This work carries out an extensive literature review of the cryptanalysis techniques, finds the gaps there in, in order to put the proposed technique in the perspective. The work also finds the applicability of Cellular Automata in cryptanalysis. A new technique has been proposed and verified for texts of around 1000 words. Each text is encrypted 10 times and then decrypted using the proposed technique. The work has also been compared with that employing Genetic Algorithm. The experiments carried out prove the veracity of the technique and paves way of Cellular automata in cryptanalysis. The paper also discusses the future scope of the work.

Innovative Genetic Algorithmic Approach to Select Potential Patches Enclosing Real and Complex Zeros of Nonlinear Equation

2021 ◽  
pp. 223-243
Author(s):  
Vijaya Lakshmi V. Nadimpalli ◽  
Rajeev Wankar ◽  
Raghavendra Rao Chillarige
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equation ◽  
Search Space ◽  
Regions Of Interest ◽  
New Method ◽  
Real Numbers ◽  
Algorithmic Approach ◽  
Space Reduction ◽  
Complex Zeros ◽  
The Given

In this article, an innovative Genetic Algorithm is proposed to find potential patches enclosing roots of real valued function f:R→R. As roots of f can be real as well as complex, the function is reframed on to complex plane by writing it as f(z). Thus, the problem now is transformed to finding potential patches (rectangles in C) enclosing z such that f(z)=0, which is resolved into two components as real and imaginary parts. The proposed GA generates two random populations of real numbers for the real and imaginary parts in the given regions of interest and no other initial guesses are needed. This is the prominent advantage of the method in contrast to various other methods. Additionally, the proposed ‘Refinement technique' aids in the exhaustive coverage of potential patches enclosing roots and reinforces the selected potential rectangles to be narrow, resulting in significant search space reduction. The method works efficiently even when the roots are closely packed. A set of benchmark functions are presented and the results show the effectiveness and robustness of the new method.

Inverse Solution to the Coupled Ordinary Differential Equation

2010 ◽  
Author(s):  
P. Venkataraman
Keyword(s):  
Differential Equation ◽  
Genetic Algorithm ◽  
Differential Equations ◽  
Generic Model ◽  
Real Numbers ◽  
Data Points ◽  
Discrete Values ◽  
Linear Differential ◽  
Discrete Programming ◽  
The Given

The identification of the actual form of the constant coefficient coupled differential equations and their boundary conditions, from two sets of discrete data points, is possible through a unique two-step approach developed in this paper. In the first step, the best Bezier function is fitted to the data. This allows an effective approximation of the data and the required number of derivatives for the entire range of the independent variable. In the second step, the known derivatives are introduced in a generic model of the coupled differential equation. This generic form includes two types of unknowns, real numbers and integers. The real numbers are the coefficients of the various terms in the differential equations, while the integers are exponents of the derivatives. The unknown exponents and coefficients are identified using an error formulation. Two examples are solved. The given data is exact, smooth and they represent solutions to coupled linear differential equations. The solution is obtained through discrete programming. Three methods are presented. The first is limited enumeration, which is useful if the coefficients belong to a limited set of discrete values. The second is global search using the genetic algorithm for a larger choice of coefficient values. The third uses a state space integrator driven by the genetic algorithm, to minimize the error between known data and that obtained from numerical integration.

Applicability of Cellular Automata in Cryptanalysis

2017 ◽  
Vol 8 (2) ◽  
pp. 38-48 ◽  
Author(s):  
Harsh Bhasin ◽  
Naved Alam
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cellular Automata ◽  
Literature Review ◽  
Search Problem ◽  
Extensive Literature ◽  
Cipher Text ◽  
Soft Computing Techniques ◽  
A New Technique ◽  
The Given

Cryptanalysis refers to finding the plaintext from the given cipher text. The problem reduces to finding the correct key from a set of possible keys, which is basically a search problem. Many researchers have put in a lot of effort to accomplish this task. Most of the efforts used conventional techniques. However, soft computing techniques like Genetic Algorithms are generally good in optimized search, though the applicability of such techniques to cryptanalysis is still a contentious point. This work carries out an extensive literature review of the cryptanalysis techniques, finds the gaps there in, in order to put the proposed technique in the perspective. The work also finds the applicability of Cellular Automata in cryptanalysis. A new technique has been proposed and verified for texts of around 1000 words. Each text is encrypted 10 times and then decrypted using the proposed technique. The work has also been compared with that employing Genetic Algorithm. The experiments carried out prove the veracity of the technique and paves way of Cellular automata in cryptanalysis. The paper also discusses the future scope of the work.

scholarly journals Optimisation of Rice Fertiliser Composition using Genetic Algorithms

2019 ◽  
Vol 2 (2) ◽  
pp. 72
Author(s):  
Retno Dewi Anissa ◽  
Wayan Firdaus Mahmudy ◽  
Agus Wahyu Widodo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Selection Process ◽  
Test Results ◽  
Random Mutation ◽  
Rice Quality ◽  
Food Scarcity ◽  
Fitness Value ◽  
Reproduction Process ◽  
Tournament Selection

There are so many problems with food scarcity. One of them is not too good rice quality. So, an enhancement in rice production through an optimal fertiliser composition. Genetic algorithm is used to optimise the composition for a more affordable price. The process of genetic algorithm is done by using a representation of a real code chromosome. The reproduction process using a one-cut point crossover and random mutation, while for the selection using binary tournament selection process for each chromosome. The test results showed the optimum results are obtained on the size of the population of 10, the crossover rate of 0.9 and the mutation rate of 0.1. The amount of generation is 10 with the best fitness value is generated is equal to 1,603.

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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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