Dynamic memory model for non-stationary optimization

被引：0
|
作者：
Bendtsen, CN [1 ]
Krink, T [1 ]
机构：
[1] Aarhus Univ, EVALife, Dept Comp Sci, DK-8000 Aarhus C, Denmark
来源：
CEC'02: PROCEEDINGS OF THE 2002 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2 | 2002年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Real-world problems are often nonstationary and can cause cyclic, repetitive patterns in the search landscape. For this class of problems, we introduce a new GA with dynamic explicit memory, which showed superior performance compared to a classic GA and a previously introduced memorybased GA for two dynamic benchmark problems.
引用
收藏
页码：145 / 150
页数：6
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