TY - GEN
T1 - Quantum-inspired multi-gene linear genetic programming model for regression problems
AU - Strachan, Guilherme C.
AU - Koshiyama, Adriano S.
AU - Dias, Douglas M.
AU - Vellasco, Marley M.B.R.
AU - Pacheco, Marco A.C.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/12
Y1 - 2014/12/12
N2 - We propose the Quantum-Inspired Multi-Gene Lin-ear Genetic Programming (QIMuLGP), which is a generalization of Quantum-Inspired Linear Genetic Programming (QILGP) model for symbolic regression. QIMuLGP allows us to explore a different genotypic representation (i.e. linear), and to use more than one genotype per individual, combining their outputs using least squares method (multi-gene approach). We used 11 benchmark problems to experimentally compare QIMuLGP with: canonical tree Genetic Programming, Multi-Gene tree-based GP (MGGP), and QILGP. QIMuLGP obtained better results than QILGP in almost all experiments performed. When compared to MGGP, QIMuLGP achieved equivalent errors for some experiments with its runtime always shorter (up to 20 times and 8 times on average), which is an important advantage in high dimensional-scalable problems.
AB - We propose the Quantum-Inspired Multi-Gene Lin-ear Genetic Programming (QIMuLGP), which is a generalization of Quantum-Inspired Linear Genetic Programming (QILGP) model for symbolic regression. QIMuLGP allows us to explore a different genotypic representation (i.e. linear), and to use more than one genotype per individual, combining their outputs using least squares method (multi-gene approach). We used 11 benchmark problems to experimentally compare QIMuLGP with: canonical tree Genetic Programming, Multi-Gene tree-based GP (MGGP), and QILGP. QIMuLGP obtained better results than QILGP in almost all experiments performed. When compared to MGGP, QIMuLGP achieved equivalent errors for some experiments with its runtime always shorter (up to 20 times and 8 times on average), which is an important advantage in high dimensional-scalable problems.
KW - Multi-gene genetic pro-gramming
KW - Quantum-inspired algorithm
KW - Symbolic regression
UR - https://www.scopus.com/pages/publications/84922531019
U2 - 10.1109/BRACIS.2014.37
DO - 10.1109/BRACIS.2014.37
M3 - Conference contribution
AN - SCOPUS:84922531019
T3 - Proceedings - 2014 Brazilian Conference on Intelligent Systems, BRACIS 2014
SP - 152
EP - 157
BT - Proceedings - 2014 Brazilian Conference on Intelligent Systems, BRACIS 2014
PB - IEEE
T2 - 3rd Brazilian Conference on Intelligent Systems, BRACIS 2014
Y2 - 19 October 2014 through 23 October 2014
ER -