Hybridizing a genetic algorithm with an artificial immune system for global optimization

被引：104

作者：

Coello, CAC ^{[1
]}

Cortés, NC ^{[1
]}

机构：

[1] Inst Politecn Nacl 2508, CINVESTAV IPN, Evolutionary Computat Grp, Dept Ingn Elect,Secc Computac, Mexico City 07300, DF, Mexico

来源：

ENGINEERING OPTIMIZATION | 2004年 / 36卷 / 05期

关键词：

artificial immune system; genetic algorithms; global optimization; parallel genetic algorithms;

D O I：

10.1080/03052150410001704845

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper proposes an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality, and inequality) in a genetic algorithm used for global optimization. The approach is implemented both in serial and parallel forms, and it is validated using several test functions taken from the specialized literature. Our results indicate that the proposed approach is highly competitive with respect to penalty-based techniques and with respect to other constraint-handling techniques which are considerably more complex to implement.

引用

页码：607 / 634

页数：28

共 49 条

[1] A socio-behavioural simulation model for engineering design optimization [J].