An mind-evolution method for solving numerical optimization problems

被引：0

作者：

Zeng, JC ^{[1
]}

Zha, K ^{[1
]}

机构：

[1] Taiyuan Heavy machinery Inst, Div Syst Simulat & Comp Applicat, Taiyuan 030024, Peoples R China

来源：

PROCEEDINGS OF THE 3RD WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-5 | 2000年

关键词：

mind-evolution; genetic algorithm; optimization;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

MEBML, Mind-Evolution-Eased Machine Learning presented in literature [1] has many superiority for solving premature convergence problem of genetic algorithm and non-numerical optimization. The similartaxis and dissimilation operators have some shortcomings and no theoretical analysis method, so that the efficiency is lower. For numerical optimization problems, the construction methods of the similartaxis and dissimilation operators are given in the paper, and the effectiveness is proven through the computing examples.

引用

页码：126 / 128

页数：3