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About 10,000 papers for “genetic method”

Genetic Method

Kai Jensen · Review of Educational Research · 1939

Cited by 1

Rapid Genetic Screening Method

The HKU Scholars Hub (University of Hong Kong) · 2007

A Rapid And Sensitive Detection Method Capable Of Reliably Distinguishing Fragments Identical To A Defined Sequence From Fragments Containing A Single Base Pair Mismatch Relative To The Defined Sequence. The Method Employs A Peptide Analog Antisense Probe, Such As A Peptide Nucleic Acid (Pna) Probe, Which Hybridizes With Dna To Forms Complexes Having Higher Thermal Stability, But Greater Sensitivity To Base Pair Mismatches, Than Corresponding Dna/Dna Complexes. The Method May Be Used In A Number Of Applications, Including Genetic Screening And Hybridization Sequencing Applications.

Cited by 0Open access

\tWhitehead Method and Genetic Algorithms

Alexei D. Miasnikov, Alexei G. Myasnikov · 2008

In this paper we discuss a genetic version (GWA) of Whitehead Algorithm, which is one of the basic algorithms in combinatorial group theory. It turns out that GWA is surprisingly fast and outperforms the standard Whitehead algorithm in free groups of rank � 5. Experimenting with GWA we collected an interesting numerical data that clarifies the time-complexity of Whitehead’s Problem in general. These experiments led us to several mathematical conjectures. If confirmed they will shed light on hidden mechanisms

Cited by 0

Whitehead Method and Genetic Algorithms

Alexei D. Myasnikov, Alexei G. Myasnikov · 2007

In this paper we discuss a genetic version (GWA) of the Whitehead's algorithm, which is one of the basic algorithms in combinatorial group theory. It turns out that GWA is surprisingly fast and outperforms the standard Whitehead's algorithm in free groups of rank 5. Experimenting with GWA we collected an interesting numerical data that clarifies the time-complexity of the Whitehead's Problem in general. These experiments led us to several mathematical conjectures. If con rmed they will shed light on hidden mechanisms of Whitehead Method and geometry of automorphic orbits in free groups.

Cited by 0

Borderless Optimization Method Genetic Algorithm

Mustafa Kaya · IntechOpen eBooks · 2023

Genetic algorithms, a stochastic research method, are Darwin’s “survival of the best.” Development of biological systems based on the principle of “survival of the fittest” emerged with the adaptation of the process to the computer environment. In genetic algorithms, processes executed are stored in computer memory similar to natural populations performed on units. Today, there are many linear and nonlinear solutions for optimization problems. The method has been developed. Designing optimization problems with these method variables is assumed to be continuous. But this acceptance does not always valid. In some problems, there are too many design variables and constraints for traditional optimization in solving such problems. The use of this method sometimes gives erroneous results or the solution time is too long. Since genetic algorithms are an intuitive method, a given may not find the optimum result for the problem. However, it optimizes the problems that cannot be solved by known methods or the solution time of the problem is too long. Initial nonlinear optimization genetic algorithms applied to the problems of traveling salesman, workshop scheduling, and syllabus preparation, such as complex optimization problems, have been successfully implemented.

Cited by 1Open access

Whitehead method and genetic algorithms

Alexei Miasnikov, Alexei Myasnikov · Contemporary mathematics - American Mathematical Society · 2004

In this paper we discuss a genetic version (GWA) of the Whitehead's algorithm, which is one of the basic algorithms in combinatorial group theory. It turns out that GWA is surprisingly fast and outperforms the standard Whitehead's algorithm in free groups of rank >= 5. Experimenting with GWA we collected an interesting numerical data that clarifies the time-complexity of the Whitehead's Problem in general. These experiments led us to several mathematical conjectures. If confirmed they will shed light on hidden mechanisms of Whitehead Method and geometry of automorphic orbits in free groups.

Cited by 18

Lecture Scheduling Using Genetic Algorithm Method

Sur Liyan, Danny Kriestanto, Aldian Ramadhan et al. · Journal of Intelligent Software Systems · 2024

Lecture scheduling at a university is a very important element, because it determines the progress of the lecture activity process. At the Indonesian Digital Technology University, the lecture scheduling process still uses Microsoft Excel, this is considered less than optimal because it takes a relatively long time, the process is long and requires a high level of accuracy, which is something that often becomes an obstacle in the scheduling process. The genetic algorithm is an algorithm that can be used to solve problems on a large scale and with a high level of complexity, such as lecture scheduling. Genetic algorithms have advantages over other optimization methods, namely that genetic algorithms can optimize problems with complex problems and a very wide search space. There are several stages in a genetic algorithm, namely: initial population initialization, fitness evaluation, selection, crossover and mutation. The results of this research show that scheduling lectures using the genetic algorithm method results in faster and more accurate results, because the process is carried out by the program by finding the best solution from each generation iteration and the process will stop when the required solution is obtained. Meanwhile, scheduling lectures using MS Excel takes longer because it is done manually with the help of the VLOOKUP formula and requires a high level of accuracy so that there are no conflicting lecture schedules. From the test results, using Python software with a genetic algorithm takes 0.609356 seconds with an accuracy level of 100%. Meanwhile, testing using MS Excel with VLOOKUP takes around 20 minutes with an accuracy rate of 95%.Keywords— Scheduling, Lectures, Genetic Algorithm

Cited by 0Open access

Enhanced Genetic Algorithm for Prony Method

Gerardo I. Torres-Cisneros, Alain Manzo-Martínez, Graciela Ramirez-Alonso et al. · 2025

We propose a Genetic Algorithm (GA) method for estimating the parameters of a sum of damped complex exponentials embedded in noise. The approximation of these parameters can be obtained using Prony analysis, which is highly effective for estimating frequency, damping, amplitude, and phase from data. However, it has limitations, including noise sensitivity and analytical complexity. The proposed approach is evaluated against two Prony-like methods: a polynomial Prony variant–total least squares (TLS)–and the matrix pencil method (MPM). It is found through simulation that the proposed algorithm outperforms the two Prony-based methods analyzed, in terms of accuracy and tolerance to noise. Furthermore, we validate the proposed method through analysis in a real-world dataset that provides high-density Electroencephalograms (EEG) recordings of visual-evoked potentials (VEPs). We argue that the evolutionary approach reduces analytical complexity while improving traditional Prony-based methods.

Cited by 0

STATISTICAL METHOD of GENETIC ASSOCIATION STUDIES

Hongjing Xie · 2022

In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes in association studies produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In our research, we develop two novel methods to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. In the first method, we cluster multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the p-value of an association test between the merged phenotype and a SNP which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated p-value for all clusters to test the association between multiple phenotypes and a SNP. In the second method, we first construct a Multi-Layer Network (MLN) using all individuals with at least one case status among all phenotypes. Then, we introduce a computational efficient community detection method to group phenotypes into different disjoint clusters based on the MLN. The phenotypes in the same cluster are merged to a single phenotype which mainly eliminates the issue of inflated type I error rate of test for extremely unbalanced binary phenotypes. Finally, to test the association between all phenotypes and a SNP, we use the score test statistic to test the association between each merged phenotype and a SNP and then use the Omnibus test to obtain an overall p-value (MLN-O). Extensive simulation studies reveal that the newly proposed approaches can control type I error rates and are more powerful than other methods we compared with. The real data analyses also show that our methods outperform other methods we compared with.

Cited by 0

“Critical or Genetic Method?” (1883)1

Alan Duncan · 2024

In the Critique of Pure Reason, which was, of course, less intended as a system of philosophy than as a “tractate on method,” Kant posed a new conception of philosophy’s task and mode of knowledge against the psychologism of his contemporaries; since then, the question of the essence of its method has not disappeared from philosophy’s agenda. This is all the more understandable because the decision of this question at the same time decides the position one is to assume within or vis-à-vis philosophy.

Cited by 0

Least squares method using genetic algorithm

Matúš Holec · Digital Repository (National Repository of Grey Literature) · 2011

This thesis describes the design and implementation of genetic algorithm for approximation of non-linear mathematical functions using the least squares method. One objective of this work is to theoretically describe the basics of genetic algorithms. The second objective is to create a program that would be potentially used to approximate empirically measured data by the scientific institutions. Besides the theoretical description of the given subject, the text part of the work mainly deals with the design of the genetic algorithm and the whole application solving the given problem. Specific part of the assignment is that the developed application has to support approximation of points by various mathematical non-linear functions in several different intervals, and then it has to insure, that resulting functions are continuous throughout all the intervals. Described functionality is not offered by any available software.

Cited by 0

Optimization Method based on Genetic Algorithms

Arturo Rangel‐Merino, J. López‐Bonilla, Roberto Linares y Miranda · 2005

The design of electromagnetic systems using methods of optimization have been carried out with deterministic methods. However, these methods are not efficient, because the object functions obtained from electromagnetic optimization problems are often highly non-linear, stiff, multiextreme and non-differential. The lack of a single method available to deal with multidimensional problems, including those with several goals to optimize, has generated the need to use numerical processes for optimization. This paper presents a method of global optimization based on genetic algorithms. The Genetic Algorithms are a versatile tool, which can be applied as a global optimization method to problems of electromagnetic engineering, because they are easy to implement to non-differentiable functions and discrete search spaces. It is also shown how, in some cases, genetic algorithms have been applied with success in electromagnetic problems, such as antenna design, far-field prediction, absorber coatings design, etc.

Cited by 37