MODELING THE GENETIC ALGORITHM BY A NONHOMOGENEOUS MARKOV CHAIN: WEAK AND STRONG ERGODICITY

被引：5

作者：

Campos, V. S. M. ^{[1
]}

Pereira, A. G. C. ^{[1
]}

Rojas Cruz, J. A. ^{[1
]}

机构：

[1] Univ Fed Rio Grande do Norte, Dept Matemat, BR-59072970 Natal, RN, Brazil

来源：

THEORY OF PROBABILITY AND ITS APPLICATIONS | 2013年 / 57卷 / 01期

关键词：

Markov chains; weak and strong ergodicity; nonhomogeneous; genetic algorithms; CONVERGENCE;

D O I：

10.1137/S0040585X97985868

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Evolutionary algorithms are used to search for optimal points of functions. One of these algorithms, the canonical genetic algorithm, uses in its dynamics two parameters, namely mutation and crossover probabilities, which are kept fixed throughout the algorithm's evolution. In this paper, changes in those parameters will be allowed and the convergence of this new algorithm will be analyzed. We also present an approach to weak ergodicity of a nonhomogeneous Markov chains without using directly Dobrushin's delta coefficient.

引用

页码：144 / U208

页数：8

共 7 条

[1] Asymptotic convergence of genetic algorithms [J].

Cerf, R .

ADVANCES IN APPLIED PROBABILITY, 1998, 30 (02) :521-550

[2]

CRUZ J. A. ROJAS, 2004, SANKHY INDIAN J STAT, V66, P243

[3]

Eiben A. E., 1991, Parallel Problem Solving from Nature. 1st Workshop, PPSN 1 Proceedings, P4

[4]

Fogel D.B., 1992, Evolving Artificial Intelligence

[5]

Holland J.H., 1992, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence

[6]

ISAACSON R. L., 1976, MARKOV CHAINS THEORY

[7] CONVERGENCE ANALYSIS OF CANONICAL GENETIC ALGORITHMS [J].

RUDOLPH, G .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (01) :96-101

← 1 →