How do you calculate mutation rate in genetic algorithm?
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| Papers (7) | Insight |
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5 Citations | This estimate can be used not only to understand the domination of genes in the representation but also to design the mutation rate in genetic algorithms (GAs). |
| We developed a genetic algorithm which selfadapts both mutation strength and population size; the results indicate that the approach works quite well. | |
16 Nov 1996 36 Citations | The results show that the performance of a genetic algorithm using large mutation rates and population-elitist selection (GALME) is superior. |
25 Jun 2020 36 Citations | There, it can relieve the algorithm designer from finding the optimal mutation rate and nevertheless obtain a performance close to the one that the optimal mutation rate gives. |
| In this study, a novel promising approach is represented with an innovated mutation method utilising genetic algorithm (GA). | |
| The periodic changes of mutation rate are found to improve the explorative abilities of the algorithm. | |
91 Citations | Experiments are reported that show the proposed algorithm performs better than most genetic algorithms with single mutation operators. |
Related Questions
What is the mutation rate in mutations per nucleotide copied of SARS-CoV-2 in mutations per nucleotide copied?5 answersThe mutation rate of SARS-CoV-2 varies across different studies. One study modeled the mutation rate as approximately 2 mutations per month per nucleotide copied, revealing distinct asymmetries in mutation rates among nucleotides and strains. Another study found that the mutation rate was substantially higher within hosts compared to population-based analyses, with rates ranging from 34.7 to 93.6 substitutions per genome per year at different allele frequencies. Additionally, a study analyzing mutations over a three-year period identified specific mutations that seeded the pandemic in Europe, with a mutation in the RNA-dependent RNA polymerase and the D614G mutation in the spike protein playing significant roles in virus fitness. These findings collectively highlight the dynamic nature of SARS-CoV-2 mutations and their impact on viral evolution.
Which models can be used to predict gene expression from mutation data?5 answersVarious models can be utilized to predict gene expression from mutation data. One approach involves leveraging multi-modal associative relationships between mutational profiles and gene expression or knockout effect profiles, reconstructing expression or effect scores for genes of interest from mutation features, and using this reconstructed representation for modeling and predicting clinical outcomes. Additionally, machine learning techniques can be employed to model mutational signatures and gene expression associations, demonstrating that gene expression and somatic mutations can translate into associations between gene expression and mutational signatures. Furthermore, deep generative models like mutationTCN, incorporating dilated causal convolutions and attention mechanisms, have shown competitive performance in predicting the effects of genetic variation from evolutionary sequence data.
How does mutation testing work?5 answersMutation testing is a fault-based testing technique that evaluates the effectiveness of a test suite in detecting faults. It involves seeding faults into the program under test and then writing tests to reveal these faults. The goal is to create tests that can detect a large number of faults, including those that couple with the seeded faults. Several approaches have been proposed to improve the efficiency of mutation testing. One approach is to automate the process using tools like Mutta, which can automate the mutation testing process for web applications. Another approach is to scale mutation testing by using distributed systems and load distribution algorithms, which can significantly reduce the computational cost. Additionally, there are efforts to adapt mutation testing for deep learning models, such as Probabilistic Mutation Testing (PMT), which addresses the stochasticity inherent in training deep learning models and allows for more consistent and informed decisions on mutations.
What are the effects of low mutation rate in the population in genetic algorithm?5 answersLow mutation rates in a population in genetic algorithms have several effects. Firstly, they promote the evolution of advantageous traits by allowing the favorable phenotypic effects of mutations to occur without interference from deleterious mutations. Secondly, low mutation rates help maintain existing beneficial mutations and prevent the occurrence of deleterious mutations, further promoting the evolution of advantageous traits. Additionally, low mutation rates can lead to the convergence of the genetic algorithm, resulting in improved performance and the ability to find optimal solutions. Furthermore, targeted mutation at individuals with lower information density can increase the diversity of alleles and the occurrence of solution alleles in the population. Finally, low mutation rates can be beneficial for populations thriving in changing environments, as a larger fraction of mutations have beneficial effects, providing the necessary diversity for adaptation.
What is the best mutation rate in genetic algorithm?5 answersThe best mutation rate in genetic algorithms depends on the specific problem being solved. For multi-modal functions, larger mutation rates have been found to give significantly better runtimes, with any mutation rate between 2/n and m/n leading to a speedup exponential in m compared to the standard choice. In contrast, a study comparing high-probability and low-probability mutation rates found that the high-probability mutation performed well on most tested functions, outperforming the low-probability mutation on some of them. Another approach is the use of individual level adaptive mutation rate control, where the mutation rate varies based on the rank of each individual within the population. This method has been shown to outperform fixed and deterministic mutation rate schemes. The results of experiments suggest that the best mutation rate for genetic algorithms lies between 5 and 20%. Additionally, allowing the mutation rate to decrease from high values to low ones has been shown to be advantageous in terms of algorithm convergence.
How does the rate of mutation of y-str change over time?3 answersThe rate of mutation of Y-STRs changes over time. Studies have shown that the mutation rate of Y-STRs is influenced by the length of the alleles. Longer alleles have a higher mutation and contraction rate compared to shorter alleles. Additionally, the mutation rate of Y-STRs is also influenced by factors such as the average allele size, the complexity of the repeat motif sequence, and the age of the father. It has been estimated that the load of de novo STR mutations is at least 75 mutations per generation, making it comparable to the load of all other known variant types. Furthermore, the mutation rate of rapidly mutating (RM) Y-STRs has been found to be higher than that of conventional Y-STRs. Overall, these findings suggest that the rate of mutation of Y-STRs is not constant and can vary depending on various factors.