TY - GEN
T1 - Universal learning machine with genetic programming
AU - Re, Alessandro
AU - Vanneschi, Leonardo
AU - Castelli, Mauro
N1 - info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT#
info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT#
Re, A., Vanneschi, L., & Castelli, M. (2019). Universal learning machine with genetic programming. In J. J. Merelo, J. Garibaldi, A. Linares-Barranco, K. Madani, K. Warwick, & K. Warwick (Eds.), Proceedings of the 11th International Joint Conference on Computational Intelligence (Vol. 1, pp. 115-122). (IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence). Viena: SciTePress.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - This paper presents a proof of concept. It shows that Genetic Programming (GP) can be used as a "universal" machine learning method, that integrates several different algorithms, improving their accuracy. The system we propose, called Universal Genetic Programming (UGP) works by defining an initial population of programs, that contains the models produced by several different machine learning algorithms. The use of elitism allows UGP to return as a final solution the best initial model, in case it is not able to evolve a better one. The use of genetic operators driven by semantic awareness is likely to improve the initial models, by combining and mutating them. On three complex real-life problems, we present experimental evidence that UGP is actually able to improve the models produced by all the studied machine learning algorithms in isolation.
AB - This paper presents a proof of concept. It shows that Genetic Programming (GP) can be used as a "universal" machine learning method, that integrates several different algorithms, improving their accuracy. The system we propose, called Universal Genetic Programming (UGP) works by defining an initial population of programs, that contains the models produced by several different machine learning algorithms. The use of elitism allows UGP to return as a final solution the best initial model, in case it is not able to evolve a better one. The use of genetic operators driven by semantic awareness is likely to improve the initial models, by combining and mutating them. On three complex real-life problems, we present experimental evidence that UGP is actually able to improve the models produced by all the studied machine learning algorithms in isolation.
KW - Ensembles
KW - Genetic programming
KW - Geometric semantic genetic programming
KW - Machine learning
KW - Master algorithm
UR - http://www.scopus.com/inward/record.url?scp=85074267111&partnerID=8YFLogxK
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000571773900010
U2 - 10.5220/0007808101150122
DO - 10.5220/0007808101150122
M3 - Conference contribution
VL - 1
T3 - IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence
SP - 115
EP - 122
BT - Proceedings of the 11th International Joint Conference on Computational Intelligence
A2 - Merelo, Juan Julian
A2 - Garibaldi, Jonathan
A2 - Linares-Barranco, Alejandro
A2 - Madani, Kurosh
A2 - Warwick, Kevin
A2 - Warwick, Kevin
PB - SciTePress - Science and Technology Publications
CY - Viena
T2 - 11th International Joint Conference on Computational Intelligence, IJCCI 2019
Y2 - 17 September 2019 through 19 September 2019
ER -