A Markov chain analysis of genetic algorithms with power of 2 cardinality alphabets

被引：10
|
作者：
Aytug, H
Bhattacharrya, S
Koehler, GJ
机构：
[1] PURDUE UNIV, KRANNERT SCH MANAGEMENT, W LAFAYETTE, IN 47907 USA
[2] SO ILLINOIS UNIV, DEPT MANAGEMENT, CARBONDALE, IL 62901 USA
[3] MICHIGAN TECHNOL UNIV, SCH BUSINESS & ENGN ADM, HOUGHTON, MI 49931 USA
来源：
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 1997年 / 96卷 / 01期
关键词：
genetic algorithm; stopping criteria; higher cardinality;
D O I：
10.1016/S0377-2217(96)00121-X
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
In this paper we model the run time behavior of GAs using higher cardinality representations as Markov Chains, define the states of the Markov Chain and derive the transition probabilities of the corresponding transition matrix. We analyze the behavior of this chain and obtain bounds on its convergence rate and bounds on the runtime complexity of the GA. We further investigate the effects of using binary versus higher cardinality representation of a search space.
引用
收藏
页码：195 / 201
页数：7
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