Genetic algorithm with elitist model and its convergence

被引：151

作者：

Bhandari, D ^{[1
]}

Murthy, CA ^{[1
]}

Pal, SK ^{[1
]}

机构：

[1] INDIAN STAT INST,MACHINE INTELLIGENCE UNIT,CALCUTTA 700035,W BENGAL,INDIA

来源：

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 1996年 / 10卷 / 06期

关键词：

genetic algorithms; Markov chains; Elitist model; transition probability matrix; single point crossover; global optimal string;

D O I：

10.1142/S0218001496000438

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

In this article, the genetic algorithm with elitist model (EGA) is modeled as a finite state Markov chain. A state in the Markov chain denotes a population together with a potential string. Proof for the convergence of an EGA to the best chromosome (string), among all possible chromosomes, is provided here. Mutation operation has been found to be essential for convergence. It has been shown that an EGA converges to the global optimal solution with any choice of initial population.

引用

页码：731 / 747

页数：17

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