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

Authors:
N. X. Hoai;R. I. McKay;D. Essam;R. Chau
Affiliations:
Sch. of Comput. Sci., Univ. of New South Wales, Canberra, ACT, Australia;Dept. of Autom. Control, Nat. Univ. of Defense Technol., Changsha, China;Dept. of Autom. Control, Nat. Univ. of Defense Technol., Changsha, China;Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore
Venue:
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Year:
2002

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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.