scholarly journals Modeling the genetic algorithm by a nonhomogeneous Markov chain: weak and strong ergodicity

2012 ◽  
Vol 57 (1) ◽  
pp. 185-192
Author(s):  
V S. M. Campos ◽  
V S. M. Campos ◽  
A G. C. Pereira ◽  
A G. C. Pereira ◽  
J A Rojas Cruz ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Markov Chain ◽  
Strong Ergodicity ◽  
Nonhomogeneous Markov Chain

Strong ergodicity for continuous-time, non-homogeneous Markov chains

1982 ◽  
Vol 19 (3) ◽  
pp. 692-694 ◽  
Author(s):  
Mark Scott ◽  
Barry C. Arnold ◽  
Dean L. Isaacson
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Continuous Time ◽  
Homogeneous Markov Chain ◽  
Strong Ergodicity

Characterizations of strong ergodicity for Markov chains using mean visit times have been found by several authors (Huang and Isaacson (1977), Isaacson and Arnold (1978)). In this paper a characterization of uniform strong ergodicity for a continuous-time non-homogeneous Markov chain is given. This extends the characterization, using mean visit times, that was given by Isaacson and Arnold.

The Study on Convergence and Convergence Rate of Genetic Algorithm Based on an Absorbing Markov Chain

2012 ◽  
Vol 239-240 ◽  
pp. 1511-1515 ◽  
Author(s):  
Jing Jiang ◽  
Li Dong Meng ◽  
Xiu Mei Xu
Keyword(s):  
Genetic Algorithm ◽  
Markov Chain ◽  
Convergence Rate ◽  
Hitting Time ◽  
Sufficient Condition ◽  
First Hitting Time ◽  
Absorbing Markov Chain ◽  
Expected Hitting Time ◽  
Optimization Efficiency ◽  
Theoretical Issues

The study on convergence of GA is always one of the most important theoretical issues. This paper analyses the sufficient condition which guarantees the convergence of GA. Via analyzing the convergence rate of GA, the average computational complexity can be implied and the optimization efficiency of GA can be judged. This paper proposes the approach to calculating the first expected hitting time and analyzes the bounds of the first hitting time of concrete GA using the proposed approach.

Modeling Human Behavior to Reduce Navigation Time of Menu Items

2015 ◽  
pp. 162-187 ◽  
Author(s):  
Tiantian Xie ◽  
Yuxi Zhu ◽  
Tao Lin ◽  
Rui Chen
Keyword(s):  
Genetic Algorithm ◽  
Markov Chain ◽  
Mobile Devices ◽  
Human Behavior ◽  
Crucial Role ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Menu Item ◽  
Structure Complexity

With the increase in the number of menu items and the menu structure complexity, users have to spend more time in locating menu items when using menu-based interfaces. Recently, adaptive menu techniques have been explored to reduce the time and menu item prediction plays a crucial role in the techniques. Unfortunately, there still lacks effective prediction models for menu items. This chapter per the authors explores the potential of three prediction models based on Markov chain in predicting top n menu items with human behavior data while interacting with menus - the users' historical menu item selections. The results show that Weighted Markov Chain using Genetic Algorithm can obtain the highest prediction accuracy and significantly decrease navigation time by 22.6% when N equals 4 as compared to the static counterpart. Two application scenarios of these models on mobile devices and desktop also demonstrated the potentials in daily usage to reduce the time spent to search target menu items.

On ergodicity and recurrence properties of a Markov chain by an application to an open jackson network

1992 ◽  
Vol 24 (02) ◽  
pp. 343-376 ◽  
Author(s):  
Arie Hordijk ◽  
Flora Spieksma
Keyword(s):  
Markov Chain ◽  
Product Form ◽  
Geometric Ergodicity ◽  
Two Dimensional ◽  
Marginal Distributions ◽  
Jackson Network ◽  
Strong Ergodicity ◽  
Key Theorem ◽  
Stieltjes Transforms

This paper gives an overview of recurrence and ergodicity properties of a Markov chain. Two new notions for ergodicity and recurrence are introduced. They are calledμ-geometric ergodicity andμ-geometric recurrence respectively. The first condition generalises geometric as well as strong ergodicity. Our key theorem shows thatμ-geometric ergodicity is equivalent to weakμ-geometric recurrence. The latter condition is verified for the time-discretised two-centre open Jackson network. Hence, the corresponding two-dimensional Markov chain isμ-geometrically and geometrically ergodic, but not strongly ergodic. A consequence ofμ-geometric ergodicity withμof product-form is the convergence of the Laplace-Stieltjes transforms of the marginal distributions. Consequently all moments converge.

Towards consensus: some convergence theorems on repeated averaging

1977 ◽  
Vol 14 (01) ◽  
pp. 89-97 ◽  
Author(s):  
S. Chatterjee ◽  
E. Seneta
Keyword(s):  
Markov Chain ◽  
Consensus Problem ◽  
Stochastic Matrices ◽  
Convergence Theorems ◽  
Strong Ergodicity ◽  
Markov Chain Theory ◽  
Inhomogeneous Markov Chain ◽  
Chain Theory ◽  
Equivalent Conditions

The problem of tendency to consensus in an information-exchanging operation is connected with the ergodicity problem for backwards products of stochastic matrices. For such products, weak and strong ergodicity, defined analogously to these concepts for forward products of inhomogeneous Markov chain theory, are shown (in contrast to that theory) to be equivalent. Conditions for ergodicity are derived and their relation to the consensus problem is considered.

scholarly journals Limit theorems for asymptotic circular mth-order Markov chains indexed by an m-rooted homogeneous tree

Filomat ◽  
2019 ◽  
Vol 33 (6) ◽  
pp. 1817-1832
Author(s):  
Huilin Huang ◽  
Weiguo Yang
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Law Of Large Numbers ◽  
Homogeneous Tree ◽  
Strong Law ◽  
Nonhomogeneous Markov Chain ◽  
Asymptotic Equipartition Property ◽  
Large Numbers ◽  
Definition Of ◽  
Rooted Homogeneous Tree

In this paper, we give the definition of an asymptotic circularmth-order Markov chain indexed by an m rooted homogeneous tree. By applying the limit property for a sequence of multi-variables functions of a nonhomogeneous Markov chain indexed by such tree, we estabish the strong law of large numbers and the asymptotic equipartition property (AEP) for asymptotic circular mth-order finite Markov chains indexed by this homogeneous tree. As a corollary, we can obtain the strong law of large numbers and AEP about the mth-order finite nonhomogeneous Markov chain indexed by the m rooted homogeneous tree.

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