Evolutionary multi-objective optimization: A historical view of the field

被引：851

作者：

Coello Coello, Carlos A. ^{[1
]}

机构：

[1] IPN, CINVESTAV, Mexico City 07738, DF, Mexico

来源：

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | 2006年 / 1卷 / 01期

关键词：

D O I：

10.1109/MCI.2006.1597059

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：

This article provides a general overview of the field now known as "evolutionary multi-objective optization," which refers to the use of evolutionary algorithms to solve problems with two or more (often conflicting) objective functions. Using as a framework the history of this discipline, we discuss some of the most respresentative algorithms that have been developed so far, as well as some of their applications. Also, we discuss some of the methodological issues related to the use of multi-objective evolutionary algorithms, as well as some of the current and future research trends in the area.

引用

页码：28 / 36

页数：9

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