Markov Chain Model of Schema Evolution and Its Application to Stationary Distribution

被引：0

作者：

Zhang, Yu-an ^{[1
]}

Ma, Qinglian ^{[2
,3
]}

Furutani, Hiroshi ^{[4
]}

机构：

[1] Qinghai Univ, Dept Comp Technol & Applicat, Xining 810016, Peoples R China

[2] Miyazaki Univ, Interdisciplinary Grad Sch Agr & Engn, Miyazaki 8892192, Japan

[3] Qinghai Engn Invest Inst, Xining 810008, Peoples R China

[4] Miyazaki Univ, Fac Engn, Miyazaki 8892192, Japan

来源：

2014 10TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | 2014年

关键词：

genetic algorithms; markov chain; mixing time; GENETIC ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Markov chain is a powerful tool for analyzing the evolutionary process of a stochastic system. To select GA parameters such as mutation rate and population size are important in practical application. The value of this parameter has a big effect on the viewpoint of Markov chain. In this paper, we consider properties of stationary distribution with mutation in GAs. We used Markov chain to calculate distribution. If the population is in linkage equilibrium, we used Wright-Fisher model to get the distribution of first order schema. We define the mixing time is the time to arrive stationary distribution. We adopt Hunter's mixing time to estimate the mixing time of the first order schema.

引用

页码：225 / 229

页数：5

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