Lossless fitness inheritance in genetic algorithms for decision trees

被引：14
|
作者：
Kalles, Dimitris [1 ,2 ]
Papagelis, Athanasios [3 ]
机构：
[1] Hellen Open Univ, Patras, Greece
[2] Open Univ Cyprus, Nicosia, Cyprus
[3] Univ Patras, Dept Comp Engn & Informat, Patras, Greece
来源：
SOFT COMPUTING | 2010年 / 14卷 / 09期
关键词：
Decision trees; Genetic algorithms; Fitness inheritance; Fitness approximation; Learning speedup; CLASSIFICATION;
D O I：
10.1007/s00500-009-0489-y
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
When genetic algorithms are used to evolve decision trees, key tree quality parameters can be recursively computed and re-used across generations of partially similar decision trees. Simply storing instance indices at leaves is sufficient for fitness to be piecewise computed in a lossless fashion. We show the derivation of the (substantial) expected speedup on two bounding case problems and trace the attractive property of lossless fitness inheritance to the divide-and-conquer nature of decision trees. The theoretical results are supported by experimental evidence.
引用
收藏
页码：973 / 993
页数：21
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