A general steady state distribution based stopping criteria for finite length genetic algorithms

被引：20
|
作者：
Pendharkar, Parag C.
Koehler, Gary J.
机构：
[1] Penn State Univ, Capital Coll, Sch Business Adm, Middletown, PA 17057 USA
[2] Univ Florida, Warrington Coll Business Adm, Gainesville, FL 32611 USA
来源：
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 2007年 / 176卷 / 03期
关键词：
genetic algorithms; heuristic search; worst-case analysis; error bounds; Markov processes;
D O I：
10.1016/j.ejor.2005.10.050
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
We propose two general stopping criteria for finite length, simple genetic algorithms based on steady state distributions, and empirically investigate the impact of mutation rate, string length, crossover rate and population size on their convergence. Our first stopping criterion is based on the second largest eigenvalue of the genetic algorithm transition matrix, and the second stopping criterion is based on minorization conditions. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码：1436 / 1451
页数：16
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