A survey of optimization by building and using probabilistic models

被引：395

作者：

Pelikan, M ^{[1
]}

Goldberg, DE ^{[1
]}

Lobo, FG ^{[1
]}

机构：

[1] Univ Illinois, Dept Gen Engn, Illinois Genet Algorithms Lab, Urbana, IL 61801 USA

来源：

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2002年 / 21卷 / 01期

关键词：

genetic and evolutionary computation; genetic algorithms; model building; decomposable problems; stochastic optimization;

D O I：

10.1023/A:1013500812258

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space. It settles the algorithms in the field of genetic and evolutionary computation where they have been originated, and classifies them into a few classes according to the complexity of models they use. Algorithms within each class are briefly described and their strengths and weaknesses are discussed.

引用

页码：5 / 20

页数：16

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