Using Genetic Algorithms to Explore Pattern Recognition in the Immune System

被引：142

作者：

Forrest, Stephanie ^{[1
]}

Javornik, Brenda ^{[1
]}

Smith, Robert E. ^{[2
]}

Perelson, Alan S. ^{[3
]}

机构：

[1] Univ New Mexico, Dept Comp Sci, Albuquerque, NM 87131 USA

[2] Univ Alabama, Dept Engn Mech, Tuscaloosa, AL 35487 USA

[3] Univ Calif Los Alamos Natl Lab, Div Theoret, Los Alamos, NM 87545 USA

来源：

EVOLUTIONARY COMPUTATION | 1993年 / 1卷 / 03期

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

genetic algorithms; immune system; pattern recognition; fitness sharing;

D O I：

10.1162/evco.1993.1.3.191

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern-recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two-pattern-recognition problems that are relevant to natural immune systems. Finally, it reviews the relation between the model and explicit fitness-sharing techniques for genetic algorithms, showing that the immune system model implements a form of implicit fitness sharing.

引用

页码：191 / 211

页数：21

共 17 条

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