D. Song, W. Ru-Chuan, F. Xiong, and Y. Le-Chan
Gene expression programming, attribution reduction, expressiontree, intelligence computing
This paper presents gene expression programming for attribution
reduction in rough set (GEP-ARRS), which designs a new GEP
code to convert attribution reduction into an expression tree and a
new ﬁtness function. Meanwhile, to solve optimal reduction, GEP-
ARRS implements a dynamic population creation strategy to reduce
the gene length of GEP to accelerate solution eﬃciency of GEP.
Through extensive experiments on mass or high-dimensional data
sets, it is shown that GEP-ARRS is apparently more advantageous
in terms of speed and quality in contrast with traditional attribution
reduction algorithms on intelligence computing.