TY - GEN
T1 - Individualized self-adaptive genetic operators with adaptive selection in genetic programming
AU - Fitzgerald, Jeannie
AU - Ryan, Conor
PY - 2013
Y1 - 2013
N2 - In this paper we investigate a new method for improving generalization performance of Genetic Programming(GP) on Binary Classification tasks. The scheme of self adaptive, individualized genetic operators combined with adaptive tournament size is designed to provide balanced, self-adaptive exploration and exploitation. We test this scheme on several benchmark Binary Classification problems and find that the proposed techniques deliver superior performance when compared with both a tuned GP configuration and a feedback adaptive GP implementation.
AB - In this paper we investigate a new method for improving generalization performance of Genetic Programming(GP) on Binary Classification tasks. The scheme of self adaptive, individualized genetic operators combined with adaptive tournament size is designed to provide balanced, self-adaptive exploration and exploitation. We test this scheme on several benchmark Binary Classification problems and find that the proposed techniques deliver superior performance when compared with both a tuned GP configuration and a feedback adaptive GP implementation.
UR - http://www.scopus.com/inward/record.url?scp=84887848187&partnerID=8YFLogxK
U2 - 10.1109/NaBIC.2013.6617868
DO - 10.1109/NaBIC.2013.6617868
M3 - Conference contribution
AN - SCOPUS:84887848187
SN - 9781479914159
T3 - 2013 World Congress on Nature and Biologically Inspired Computing, NaBIC 2013
SP - 232
EP - 237
BT - 2013 World Congress on Nature and Biologically Inspired Computing, NaBIC 2013
T2 - 2013 World Congress on Nature and Biologically Inspired Computing, NaBIC 2013
Y2 - 12 August 2013 through 14 August 2013
ER -