scholarly journals PARAMETER VARIATION FOR LINEAR EQUATION SOLVER USING GENETIC ALGORITHM

2017 ◽  
Vol 15 (2) ◽  
pp. 42-50
Author(s):  
A M IKOTUN ◽  
A T AKINWALE ◽  
O T AROGUNDADE
Keyword(s):  
Genetic Algorithm ◽  
Linear Equation ◽  
Population Size ◽  
Linear Equations ◽  
Large Population ◽  
Small Population ◽  
Simultaneous Linear Equation ◽  
Solution Generation ◽  
Systems Of Linear Equations ◽  
Linear Equation Solver

Genetic Algorithm has been successfully applied for solving systems of Linear Equations; however the effects of varying the various Genetic Algorithms parameters on the GA systems of Linear Equations solver have not been investigated. Varying the GA parameters produces new and exciting information on the behaviour of the GA Linear Equation solver. In this paper,  a general introduction on the Genetic Algorithm, its application on finding solutions to the Systems of Linear equation as well as the effects of varying the Population size and Number of Generation is presented. The genetic algorithm simultaneous linear equation solver program was run several times using different sets of simultaneous linear equation while varying the population sizes as well as the number of generations in order to observe their effects on the solution generation. It was observed that small population size does not produce perfect solutions as fast as when large population size is used and small or large number of generations did not really have much impact on the attainment of perfect solution as much as population size. 

scholarly journals Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 390 ◽  
Author(s):  
Ahmad Hassanat ◽  
Khalid Almohammadi ◽  
Esra’a Alkafaween ◽  
Eman Abunawas ◽  
Awni Hammouri ◽  
...  
Keyword(s):  
Population Size ◽  
Optimization Problems ◽  
Large Population ◽  
Small Population ◽  
Mutation Rates ◽  
Mutation Operators ◽  
Tuning Methods ◽  
Two Parameters ◽  
The Impact ◽  
Crossover And Mutation

Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operators. This paper reviews various methods for choosing mutation and crossover ratios in GAs. Next, we define new deterministic control approaches for crossover and mutation rates, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic Decreasing of High Crossover (ILM/DHC). The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress, where (DHM/ILC) starts with 100% ratio for mutations, and 0% for crossovers. Both mutation and crossover ratios start to decrease and increase, respectively. By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers. (ILM/DHC) worked the same but the other way around. The proposed approach was compared with two parameters tuning methods (predefined), namely fifty-fifty crossover/mutation ratios, and the most common approach that uses static ratios such as (0.03) mutation rates and (0.9) crossover rates. The experiments were conducted on ten Traveling Salesman Problems (TSP). The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was found to be more effective when using large population size. In fact, both proposed dynamic methods outperformed the predefined methods compared in most cases tested.

Comparison of Genetic Algorithm and Determinant Method on Solving a System of Linear Equations

2014 ◽  
Vol 635-637 ◽  
pp. 1700-1706
Author(s):  
Sambourou Massinanke ◽  
Chao Zhu Zhang
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Linear Equation ◽  
Linear Equations ◽  
System Of Linear Equations ◽  
Almost All

Many methods are proposed to solve a system of linear equations (SLE), some are relatively efficient, in this study we use one type of Evolutionary Algorithms to solve a System of Linear Equation, the famous one: Genetic Algorithm (GA), we compare the efficiency of Genetic Algorithm and the determinant method (DM) in solving the system of linear equations, our experiences show that GA outperforms DM in almost all the cases.

scholarly journals Population size influences the type of nucleotide variations in humans

BMC Genetics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Sankar Subramanian
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
High Frequency ◽  
Genetic Diseases ◽  
Large Population ◽  
Low Frequency ◽  
Small Population ◽  
Effective Population ◽  
Genome Data ◽  
Large Populations

Abstract Background It is well known that the effective size of a population (Ne) is one of the major determinants of the amount of genetic variation within the population. However, it is unclear whether the types of genetic variations are also dictated by the effective population size. To examine this, we obtained whole genome data from over 100 populations of the world and investigated the patterns of mutational changes. Results Our results revealed that for low frequency variants, the ratio of AT→GC to GC→AT variants (β) was similar across populations, suggesting the similarity of the pattern of mutation in various populations. However, for high frequency variants, β showed a positive correlation with the effective population size of the populations. This suggests a much higher proportion of high frequency AT→GC variants in large populations (e.g. Africans) compared to those with small population sizes (e.g. Asians). These results imply that the substitution patterns vary significantly between populations. These findings could be explained by the effect of GC-biased gene conversion (gBGC), which favors the fixation of G/C over A/T variants in populations. In large population, gBGC causes high β. However, in small populations, genetic drift reduces the effect of gBGC resulting in reduced β. This was further confirmed by a positive relationship between Ne and β for homozygous variants. Conclusions Our results highlight the huge variation in the types of homozygous and high frequency polymorphisms between world populations. We observed the same pattern for deleterious variants, implying that the homozygous polymorphisms associated with recessive genetic diseases will be more enriched with G or C in populations with large Ne (e.g. Africans) than in populations with small Ne (e.g. Europeans).

scholarly journals Evolutionary Method of Detecting Intrusions using Different Population Dynamics

2020 ◽  
Vol 9 (5) ◽  
pp. 1924-1931
Keyword(s):  
Population Dynamics ◽  
Population Size ◽  
Large Population ◽  
Computation Time ◽  
Small Population ◽  
Vital Role ◽  
Local Optima ◽  
Population Sizes ◽  
Speed Up ◽  
Dynamic Population

Chaos theory plays a vital role in any evolutionary based algorithms for avoiding the local optima and to improve the convergence speed. Various researchers have used different methods to increase the detection rate and to speed up the convergence. Some researchers have used evolutionary algorithms for the same purpose and has proved that the application of those algorithms provide good results. Most of the researchers have used population sizes which remains constant throughout the evolution. It has been seen that small population size may result in premature convergence and large population size requires more computation time to find a solution. In this paper, a novel application of different population dynamics to the genetic programming (GP) algorithm has been applied to manage the population size. The main focus was to improve the accuracy of the normal GP algorithm by varying the population sizes at each generation. The experiments were conducted on the standard GP algorithm using static and dynamic population sizes. Different population dynamics has been used to check the effectiveness of the proposed algorithm. The results obtained has shown that dynamic population size gives better results compared to static population size and also solves the problem of local optima.

scholarly journals Clear: Composition of Likelihoods for Evolve And Resequence Experiments

2016 ◽  
Author(s):  
Arya Iranmehr ◽  
Ali Akbari ◽  
Christian Schlötterer ◽  
Vineet Bafna
Keyword(s):  
Population Size ◽  
Time Series Data ◽  
Large Population ◽  
Small Population ◽  
Whole Genome Sequence ◽  
Series Data ◽  
Model Parameters ◽  
Whole Genome ◽  
Population Sampling ◽  
Wide Range

AbstractThe advent of next generation sequencing technologies has made whole-genome and whole-population sampling possible, even for eukaryotes with large genomes. With this development, experimental evolution studies can be designed to observe molecular evolution “in-action” via Evolve-and-Resequence (E&R) experiments. Among other applications, E&R studies can be used to locate the genes and variants responsible for genetic adaptation. Existing literature on time-series data analysis often assumes large population size, accurate allele frequency estimates, and wide time spans. These assumptions do not hold in many E&R studies.In this article, we propose a method-Composition of Likelihoods for Evolve-And-Resequence experiments (Clear)–to identify signatures of selection in small population E&R experiments. Clear takes whole-genome sequence of pool of individuals (pool-seq) as input, and properly addresses heterogeneous ascertainment bias resulting from uneven coverage. Clear also provides unbiased estimates of model parameters, including population size, selection strength and dominance, while being computationally efficient. Extensive simulations show that Clear achieves higher power in detecting and localizing selection over a wide range of parameters, and is robust to variation of coverage. We applied Clear statistic to multiple E&R experiments, including, data from a study of D. melanogaster adaptation to alternating temperatures and a study of outcrossing yeast populations, and identified multiple regions under selection with genome-wide significance.

Optimal Configuration of a Genetic Algorithm for Command Space Layout Analysis

2018 ◽  
Vol 62 (1) ◽  
pp. 383-387 ◽  
Author(s):  
Wenbi Wang
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Simulation Experiment ◽  
Large Population ◽  
Spatial Layout ◽  
Search Efficiency ◽  
Medium Level ◽  
Military Command ◽  
Space Layout ◽  
The Impact

A genetic algorithm was developed to optimize the spatial layout of military command centres. This paper describes a simulation experiment in which the impact of key algorithm parameters on its search efficiency was examined. The results confirmed the benefit of a large population size and a long evolution process for improving the search effectiveness. For the parameter that controls the rate of introducing new solutions (i.e., probability of swap), a medium level configuration was found to be superior. Results of this study provide guidelines and heuristics for configuring key parameters of the proposed algorithm so that its search efficiency and computational expense are best balanced.

Assessing the Relevance of Processing Building Blocks in Evolutionary Computation: Experiments with Linear Systems of Equations

1999 ◽  
Vol 3 (5) ◽  
pp. 394-400
Author(s):  
David B. Fogel ◽  
◽  
Peter J. Angeline ◽  
Keyword(s):  
Evolutionary Computation ◽  
Linear Equations ◽  
Building Blocks ◽  
Small Population ◽  
Systems Of Equations ◽  
Uniform Crossover ◽  
Population Sizes ◽  
Systems Of Linear Equations ◽  
Linear Systems Of Equations ◽  
Better Than

Experiments are conducted to assess the utility of processing building blocks within a framework of evolutionary computation. Systems of linear equations are used for testing the efficiency of different recombination operators, including one- and two-point and uniform crossover. The consistent results indicate that uniform crossover, which disrupts building blocks maximally, generates statistically significantly better solutions than one- or two-point crossover. Moreover, for the cases of small population sizes, crossing over existing solutions with completely random solutions (i.e., macromutation) can perform as well or better than the traditional oneand two-point operators. The results do not support the building block hypothesis.

Mathematical Exploration: All Shades Are the Right Shade

2010 ◽  
Vol 16 (1) ◽  
pp. 56-60
Author(s):  
Stephanie S. Reilly
Keyword(s):  
Linear Equation ◽  
Linear Inequality ◽  
Linear Equations ◽  
Linear Inequalities ◽  
Algebra 1 ◽  
System Of Linear Equations ◽  
System Of Linear Inequalities ◽  
Mathematical Exploration ◽  
Systems Of Linear Equations ◽  
The Right

Students spend a lot of time in algebra 1 solving linear equations and systems of linear equations. These subtopics of algebra can be problematic and difficult for students to grasp. After months of finding the solution to a linear equation (a line) and finding the solution to a system of linear equations (generally, a point), students struggle with understanding the solution to a linear inequality or a system of linear inequalities (a shaded region). Students might think, Why do we shade at all, what does the shading mean, and why is an overlapping shaded region the solution in an inequalities systems graph?

scholarly journals The spatial Muller’s ratchet: surfing of deleterious mutations during range expansion

2019 ◽  
Author(s):  
Félix Foutel-Rodier ◽  
Alison Etheridge
Keyword(s):  
Population Size ◽  
Range Expansion ◽  
Allee Effect ◽  
Large Population ◽  
Small Population ◽  
Deleterious Mutations ◽  
Muller's Ratchet ◽  
Muller’S Ratchet ◽  
Expansion Load ◽  
The Impact

AbstractDuring a range expansion, deleterious mutations can “surf” on the colonisation front. The resultant decrease in fitness is known as expansion load. An Allee effect is known to reduce the loss of genetic diversity of expanding populations, by changing the nature of the expansion from “pulled” to “pushed”. We study the impact of an Allee effect on the formation of an expansion load with a new model, in which individuals have the genetic structure of a Muller’s ratchet. A key feature of Muller’s ratchet is that the population fatally accumulates deleterious mutations due to the stochastic loss of the fittest individuals, an event called a click of the ratchet. We observe fast clicks of the ratchet at the colonization front owing to small population size, followed by a slow fitness recovery due to migration of fit individuals from the bulk of the population, leading to a transient expansion load. For large population size, we are able to derive quantitative features of the expansion wave, such as the wave speed and the frequency of individuals carrying a given number of mutations. Using simulations, we show that the presence of an Allee effect reduces the rate at which clicks occur at the front, and thus reduces the expansion load.

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