Growth Probability of an Additional Zygote with a Beneficial Reversal Allele for the Overdominant Case in a Diploid, Coupled, Discrete-time, Mutation-selection Model

길원평 한국물리학회 2015 새물리 Vol.65 No.9

The growth probability of an additional zygote with a beneficial reversal allele was calculated in a diploid, coupled, discrete-time, mutation-selection model. The growth probability for various fitness parameters and dominance parameters in the stochastic region for the overdominant case was approximately inversely proportional to the measuring parameter, C, when C < 1/Ns*, was curved when C ≈ 1/Ns*, and was saturated when C > 1/Ns*, where N is the population size and s is the effective selective advantage of the reversal allele over the optimal allele. We suggested that the dynamic property in the stochastic region for the overdominant case in the diploid, coupled, discretetime, mutation-selection model could be approximated by using the haploid, coupled, discrete-time, mutation-selection model with a positive selective advantage. The saturated growth probability increased with increasing fitness parameter or increasing dominance parameter.

집단의 개체 수가 유한한 네 상태 반수체 결합 이산-시간 돌연변이-자연선택 모델에서 유리한 반전 대립유전자를 가진 추가 자손의 성장 확률

길원평 한국물리학회 2020 새물리 Vol.70 No.12

Growth probabilities of an additional offspring with a beneficial reversal allele were calculated by computer simulation for various population sizes, sequence lengths, selective advantages, and measuring parameters for a finite population in the four-state haploid coupled discrete-time mutation-selection (HCDMS) model. The mutation rates between all sequence elements were set to be equal. This study suggested that the boundary between the deterministic and the stochastic regions in the four-state HCDMS model could be determined by using the same criterion as that in the two-state HCDMS model. For various population sizes, sequence lengths, measuring parameters, and selective advantages, the growth probabilities in the stochastic region could be described using the theoretical formula for the growth probability in the Wright-Fisher two-allele model. 집단의 개체 수가 유한한 경우, 네 상태 반수체 결합 이산-시간 돌연변이-자연선택 (haploid coupled discrete-time mutation-selection, HCDMS) 모델에서 다양한 집단 크기, 서열 (sequence) 길이, 선택이익 (selective advantage), 측정 변수에 대해서 유익한 반전 대립유전자 (reversal allele) 를 가진 추가자손의 성장 확률 (growth probability) 을 컴퓨터 시늉내기로 계산하였다. 모든 서열원소 사이의돌연변이율을 같게 놓았다. 본 연구는 네 상태 HCDMS 모델에서 결정론적 영역과 확률적 영역 사이의경계를 두 상태 HCDMS 모델에서와 동일한 기준으로 정할 수 있음을 시사했다. 네 상태 HCDMS 모델에서 다양한 집단 크기, 서열 길이, 측정 변수, 선택 이익에 대해서 확률적 영역에서의 성장 확률이라이트-피셔 두-대립유전자 (Wright-Fisher two-allele) 모델의 성장 확률에 대한 이론식을 사용하여설명될 수 있었다.

A model for the clustered distribution of SNPs in the human genome

Pergamon 2016 Computational biology and chemistry Vol.64 No.-

<P>Motivated by a non-random but clustered distribution of SNPs, we introduce a phenomenological model to account for the clustering properties of SNPs in the human genome. The phenomenological model is based on a preferential mutation to the closer proximity of existing SNPs. With the Hapmap SNP data, we empirically demonstrate that the preferential model is better for illustrating the clustered distribution of SNPs than the random model. Moreover, the model is applicable not only to autosomes but also to the X chromosome, although the X chromosome has different characteristics from autosomes. The analysis of the estimated parameters in the model can explain the pronounced population structure and the low genetic diversity of the X chromosome. In addition, correlation between the parameters reveals the population-wise difference of the mutation probability. These results support the mutational non independence hypothesis against random mutation. (C) 2016 Elsevier Ltd. All rights reserved.</P>

코시 분포의 축척 매개변수를 추정하여 돌연변이 연산에 적용한 진화 프로그래밍

이창용(Chang-Yong Lee) 한국정보과학회 2010 정보과학회논문지 : 소프트웨어 및 응용 Vol.37 No.9

진화 프로그래밍은 실수형 최적화 문제에 널리 사용되는 알고리즘으로 돌연변이 연산이 중요한 연산이다. 일반적으로 돌연변이 연산은 확률 분와 이에 따른 매개변수를 사용하여 변수값을 변화시키는데, 이 때 매개변수 역시 돌연변이 연산의 대상이 됨으로 이를 위한 또 다른 매개변수가 필요하다. 그러나 최적의 매개변수 값은 주어진 문제에 전적으로 의존하기 때문에 매개변수 개수가 많은 경우 매개변수값들에 대한 최적 조합을 찾기 어렵다. 이러한 문제를 부분적으로나마 해결하기 위하여 본 논문에서는 변수의 돌연변이 연산을 위한 매개변수를 자기 적응적 관점에서 이론적으로 추정한 돌연변이 연산을 제안하였다. 제안한 알고리즘에서는 코시 확률 분포의 축척 매개변수를 추정하여 돌연변이 연산에 적용함으로 축척 매개변수에 대한 돌연변이 연산이 필요하지 않다는 장점이 있다. 제안한 알고리즘을 벤치마킹 문제에 적용한 실험 결과를 통해 볼 때, 최적값 측면에서는 제안한 알고리즘의 상대적 우수성은 벤치마킹 문제에 의존하였으나 계산 시간 측면에서는 모든 벤치마킹 문제에 대하여 제안한 알고리즘이 우수하였다. The mutation operation is the main operation in the evolutionary programming which has been widely used for the optimization of real valued function. In general, the mutation operation utilizes both a probability distribution and its parameter to change values of variables, and the parameter itself is subject to its own mutation operation which requires other parameters. However, since the optimal values of the parameters entirely depend on a given problem, it is rather hard to find an optimal combination of values of parameters when there are many parameters in a problem. To solve this shortcoming at least partly, if not entirely, in this paper, we propose a new mutation operation in which the parameter for the variable mutation is theoretically estimated from the self-adaptive perspective. Since the proposed algorithm estimates the scale parameter of the Cauchy probability distribution for the mutation operation, it has an advantage in that it does not require another mutation operation for the scale parameter. The proposed algorithm was tested against the benchmarking problems. It turned out that, although the relative superiority of the proposed algorithm from the optimal value perspective depended on benchmarking problems, the proposed algorithm outperformed for all benchmarking problems from the perspective of the computational time.

Fault Diagnosis of Coal Mine Equipment Based on Improved GA Optimized BP Neural Network

Jiangang Jin 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.5

유전 알고리즘의 조기수렴 저감을 위한 연산자 소인방법 연구

이홍규,Lee, Hong-Kyu 제어로봇시스템학회 2011 제어·로봇·시스템학회 논문지 Vol.17 No.12

GA (Genetic Algorithms) are efficient for searching for global optima but may have some problems such as premature convergence, convergence to local extremum and divergence. These phenomena are related to the evolutionary operators. As population diversity converges to low value, the search ability of a GA decreases and premature convergence or converging to local extremum may occur but population diversity converges to high value, then genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we analyze the effects of the selection operator, crossover operator, and mutation operator on convergence properties, and propose the sweeping method of mutation probability and elitist propagation rate to maintain the diversity of the GA's population for getting out of the premature convergence. Results of simulation studies verify the feasibility of using these sweeping operators to avoid premature convergence and convergence to local extrema.

An Improved Evolutionary Strategy of Genetic Algorithm and a New Method on Generation of Initial Population When Using Genetic Algorithms for Solving Constrained Optimization Problems

Xu Sun1,Fulin Wang,Shifa Wen 보안공학연구지원센터 2014 International Journal of Hybrid Information Techno Vol.7 No.4

Exploring Appropriate Reference Intervals and Clinical Decision Limits for Glucose-6-Phosphate Dehydrogenase Activity in Individuals From Guangzhou, China

Huang Zhenyi,Li Ziyan,Li Yating,Cao Yunshan,Zhong Suping,Liu Jinlu,Lin Zhiqian,Lin Lijuan,Fang Yanping,Zeng Jing,Su Zhaoying,Li Huibin,Liang Jianfen,Zhu Biqing,Lin Zipei,Huang Yongxin,Yang Xuexi,Jiang 대한진단검사의학회 2024 Annals of Laboratory Medicine Vol.44 No.6

Background: Quantitative detection of glucose-6-phosphate dehydrogenase (G6PD) is commonly done to screen for G6PD deficiency. However, current reference intervals (RIs) of G6PD are unsuitable for evaluating G6PD-activity levels with local populations or associating G6PD variants with hemolysis risk to aid clinical decision-making. We explored appropriate RIs and clinical decision limits (CDLs) for G6PD activity in individuals from Guangzhou, China. Methods: We enrolled 5,852 unrelated individuals between 2020 and 2022 and screened their samples in quantitative assays for G6PD activity. We conducted further investigations, including G6PD genotyping, thalassemia genotyping, follow-up analysis, and statistical analysis, for different groups. Results: In Guangzhou, the RIs for the G6PD activities were 11.20–20.04 U/g Hb in male and 12.29–23.16 U/g Hb in female. The adjusted male median and normal male median (NMM) values were 15.47 U/g Hb and 15.51 U/g Hb, respectively. A threshold of 45% of the NMM could be used as a CDL to estimate the probability of G6PD variants. Our results revealed high hemolysis-risk CDLs (male: <10% of the NMM, female: <30% of the NMM), medium hemolysis-risk CDLs (male: 10%–45% of the NMM, female: 30%–79% of the NMM), and low hemolysis-risk CDLs (male: ≥ 45% of the NMM, female: ≥ 79% of the NMM). Conclusions: Collectively, our findings contribute to a more accurate evaluation of G6PDactivity levels within the local population and provide valuable insights for clinical decisionmaking. Specifically, identifying threshold values for G6PD variants and hemolysis risk enables improved prediction and management of G6PD deficiency, ultimately enhancing patient care and treatment outcomes.