Multi-branches genetic programming as a tool for function approximation

被引：0

作者：

Rodríguez-Vázquez, K ^{[1
]}

Oliver-Morales, C ^{[1
]}

机构：

[1] Univ Nacl Autonoma Mexico, IIMAS, Circuito Escolar, Mexico City 04510, DF, Mexico

来源：

GENETIC AND EVOLUTIONARY COMPUTATION GECCO 2004 , PT 2, PROCEEDINGS | 2004年 / 3103卷

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This work presents a performance analysis of a Multi-Branches Genetic Programming (MBGP) approach applied in symbolic regression (e.g. function approximation) problems. Genetic Programming (GP) has been previously applied to this kind of regression. However, one of the main drawbacks of GP is the fact that individuals tend to grow in size through the evolution process without a significant improvement in individual performance. In Multi-Branches Genetic Programming (MBGP), an individual is composed of several branches, each branch can evolve a part of individual solution, and final solution is composed of the integration of these partial solutions.

引用

页码：719 / 721

页数：3