A Comparison of Differential Evolution and Genetic Algorithms for the Column Subset Selection Problem

被引：2

作者：

Kromer, Pavel ^{[1
,2
]}

Platos, Jan ^{[1
,2
]}

机构：

[1] VSB Tech Univ Ostrava, IT4Innovat, Ostrava, Czech Republic

[2] VSB Tech Univ Ostrava, Dept Comp Sci, Ostrava, Czech Republic

来源：

PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS, CORES 2015 | 2016年 / 403卷

关键词：

Differential evolution; Genetic algorithms; Column subset selection;

D O I：

10.1007/978-3-319-26227-7_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The column subset selection problem is a well-known complex optimization problem that has a number of appealing real-world applications including network and data sampling, dimension reduction, and feature selection. There are a number of traditional deterministic and randomized heuristic algorithms for this problem. Recently, it has been tackled by a variety of bio-inspired and evolutionary methods. In this work, differential evolution, a popular and successful real-parameter optimization algorithm, adapted for fixed-length subset selection, is used to find solutions to the column subset selection problem. Its results are compared to a recent genetic algorithm designed for the same purpose.

引用

页码：223 / 232

页数：10

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