Review of Knowledge Guidance in Intelligent Optimization Approaches

被引：1
|
作者：
Xing, Lining [1 ]
机构：
[1] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha 410073, Hunan, Peoples R China
来源：
PROCEEDINGS OF THE 2015 CHINESE INTELLIGENT AUTOMATION CONFERENCE: INTELLIGENT INFORMATION PROCESSING | 2015年 / 336卷
关键词：
Artificial intelligence; Intelligent optimization approaches; Knowledge guidance; LEARNABLE EVOLUTION MODEL; GENETIC ALGORITHM; MEMORY; DESIGN; SYSTEM; METHODOLOGY; SOLVE;
D O I：
10.1007/978-3-662-46469-4_30
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The interaction between learning and evolution has recently become a popular research direction. Many scholars make use of knowledge to strengthen the guidance process in intelligent optimization methods. We review knowledge guidance in intelligent optimization approaches, which is normally carried out through artificial intelligence approaches and special knowledge models. Some researchers have also proposed algorithms with a double-layer evolution mechanism. These improved methods are able to discover some knowledge from previous iterations and to use the discovered knowledge to guide subsequent iterations.
引用
收藏
页码：287 / 295
页数：9
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