TY - GEN
T1 - On improving grammatical evolution performance in symbolic regression with attribute grammar
AU - Karim, Muhammad Rezaul
AU - Ryan, Conor
PY - 2014
Y1 - 2014
N2 - This paper shows how attribute grammar (AG) can be used with Grammatical Evolution (GE) to avoid invalidators in the symbolic regression solutions generated by GE. In this paper, we also show how interval arithmetic can be implemented with AG to avoid selection of certain arithmetic operators or transcendental functions, whenever necessary to avoid infinite output bounds in the solutions. Results and analysis demonstrate that with the proposed extensions, GE shows significantly less overfitting than standard GE and Koza's GP, on the tested symbolic regression problems.
AB - This paper shows how attribute grammar (AG) can be used with Grammatical Evolution (GE) to avoid invalidators in the symbolic regression solutions generated by GE. In this paper, we also show how interval arithmetic can be implemented with AG to avoid selection of certain arithmetic operators or transcendental functions, whenever necessary to avoid infinite output bounds in the solutions. Results and analysis demonstrate that with the proposed extensions, GE shows significantly less overfitting than standard GE and Koza's GP, on the tested symbolic regression problems.
KW - Attribute grammar
KW - Symbolic regression
UR - http://www.scopus.com/inward/record.url?scp=84905672896&partnerID=8YFLogxK
U2 - 10.1145/2598394.2598488
DO - 10.1145/2598394.2598488
M3 - Conference contribution
AN - SCOPUS:84905672896
SN - 9781450328814
T3 - GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
SP - 139
EP - 140
BT - GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery
T2 - 16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion
Y2 - 12 July 2014 through 16 July 2014
ER -