Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: the comparative results

Author

Hoai, N.X. ; McKay, R.I. ; Essam, D. ; Chau, R.

Author_Institution

Sch. of Comput. Sci., Univ. of New South Wales, Canberra, ACT, Australia

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1326

Lastpage

1331

Abstract

In this paper, we show some experimental results of tree-adjunct grammar-guided genetic programming (TAG3P) on the symbolic regression problem, a benchmark problem in genetic programming. We compare the results with genetic programming (GP) and grammar-guided genetic programming (GGGP). The results show that TAG3P significantly outperforms GP and GGGP on the target functions attempted in terms of the probability of success. Moreover, TAG3P still performed well when the structural complexity of the target function was scaled up

Keywords

context-free grammars; functions; genetic algorithms; problem solving; programming; software performance evaluation; statistical analysis; symbol manipulation; trees (mathematics); TAG3P; performance; structural complexity scaling; success probability; symbolic regression problem; target functions; tree-adjunct grammar-guided genetic programming; Australia; Bioinformatics; Classification tree analysis; Computer science; Evolutionary computation; Genetic mutations; Genetic programming; Genomics; Performance evaluation; Regression tree analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7282-4

Type

conf

DOI

10.1109/CEC.2002.1004435

Filename

1004435