A New Method for Simplifying Algebraic Expressions in Genetic Programming Called Equivalent Decision Simplification

被引：5
|
作者：
Mori, Naoki [1 ]
McKay, Bob [2 ]
Hoai, Nguyen Xuan [2 ]
Essam, Daryl [3 ]
Takeuchi, Saori [4 ]
机构：
[1] Osaka Prefecture Univ, Osaka, Japan
[2] Seoul Natl Univ, Seoul, South Korea
[3] Univ New South Wales ADFA, Canberra, ACT, Australia
[4] Mitsubishi Electr Corp, Amagasaki, Hyogo, Japan
来源：
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 2009年 / 13卷 / 03期
关键词：
genetic programming; subtree entropy; equivalent decision simplification; diversity;
D O I：
10.20965/jaciii.2009.p0237
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Symbolic Regression is one of the most important applications of Genetic Programming, but suffers from one of the key issues in Genetic Programming, bloat. For a variety of reasons, reliable techniques to remove bloat are highly desirable. This paper introduces a novel approach of removing bloat, Equivalent Decision Simplification, in which subtrees are evaluated over the set of regression points. The effectiveness of the proposed method is confirmed by computer simulation taking simple Symbolic Regression problems as examples.
引用
收藏
页码：237 / 244
页数：8
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