HIGH-ORDER EDA

被引：0

作者：

Zeng, Jin ^{[1
]}

Ren, Qing-Sheng ^{[2
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Math, Shanghai 200240, Peoples R China

[2] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200040, Peoples R China

来源：

PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年

关键词：

Estimation of distribution algorithm (EDA); High-order EDA; Convergence; Constraint optimization;

D O I：

10.1109/ICMLC.2009.5212795

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we investigate the usage of history information for estimation of distribution algorithm (EDA). In EDA, the distribution is estimated from a set of selected individuals and then the estimated distribution model is used to generate new individuals. It needs large population size to converge to the global optimum. A new algorithm, the high-order EDA, is proposed based on the idea of filter. By the usage of history information, it can converge to the global optimum with high probability even with small population size. Convergence properties are then discussed. We also show the application for constrained optimization problems.

引用

页码：3616 / +

页数：2

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