Problem Difficulty and Code Growth in Genetic Programming
Problem Difficulty and Code Growth in Genetic Programming
复制标题
遗传编程中的问题难度和代码增长
DOI：
10.1023/b:genp.0000030194.98244.e3
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发表时间：
2004
影响因子：
2.6
通讯作者：
G. Kendall
中科院分区：
文献类型：
--
作者：
Steven M. Gustafson;Anikó Ekárt;E. Burke;G. Kendall
本文研究了遗传规划中代码增长与问题难度之间的关系。符号回归问题域使用两种不同类型的实例难度增加来研究这种关系。简化的遗传规划模型支持了这一结果，并表明难度的增加会导致更高的选择压力和更少的遗传多样性，这两者都有助于提高代码的生长速度。
This paper investigates the relationship between code growth and problem difficulty in genetic programming. The symbolic regression problem domain is used to investigate this relationship using two different types of increased instance difficulty. Results are supported by a simplified model of genetic programming and show that increased difficulty induces higher selection pressure and less genetic diversity, which both contribute toward an increased rate of code growth.
DOI：
--
发表时间：
1992
期刊：
--
影响因子：
--
作者：
J. Koza
通讯作者：
J. Koza