Symbolic regression problems by genetic programming with multi-branches

被引：0
|
作者：
Morales, CO [1 ]
Vázquez, KR [1 ]
机构：
[1] Univ Nacl Autonoma Mexico, IIMAS, DISCA, Mexico City 04510, DF, Mexico
来源：
MICAI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE | 2004年 / 2972卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This work has the aim of exploring the area of symbolic regression problems by means of Genetic Programming. It is known that symbolic regression is a widely used method for mathematical function approximation. Previous works based on Genetic Programming have already dealt with this problem, but considering Koza's GP approach. This paper introduces a novel GP encoding based on multi-branches. In order to show the use of the proposed multi-branches representation, a set of testing equations has been selected. Results presented in this paper show the advantages of using this novel multi-branches version of GP.
引用
收藏
页码：717 / 726
页数：10
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