TY - GEN
T1 - Semantic learning machine
T2 - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015
AU - Gonçalves, Ivo
AU - Silva, Sara Guilherme Oliveira da
AU - Fonseca, Carlos M.
PY - 2015
Y1 - 2015
N2 - Geometric Semantic Genetic Programming (GSGP) is a recently proposed form of Genetic Programming in which the fitness landscape seen by its variation operators is unimodal with a linear slope by construction and, consequently, easy to search. This is valid across all supervised learning problems. In this paper we propose a feedforward Neural Network construction algorithm derived from GSGP. This algorithm shares the same fitness landscape as GSGP, which allows an efficient search to be performed on the space of feedforward Neural Networks, without the need to use backpropagation. Experiments are conducted on real-life multidimensional symbolic regression datasets and results show that the proposed algorithm is able to surpass GSGP, with statistical significance, in terms of learning the training data. In terms of generalization, results are similar to GSGP.
AB - Geometric Semantic Genetic Programming (GSGP) is a recently proposed form of Genetic Programming in which the fitness landscape seen by its variation operators is unimodal with a linear slope by construction and, consequently, easy to search. This is valid across all supervised learning problems. In this paper we propose a feedforward Neural Network construction algorithm derived from GSGP. This algorithm shares the same fitness landscape as GSGP, which allows an efficient search to be performed on the space of feedforward Neural Networks, without the need to use backpropagation. Experiments are conducted on real-life multidimensional symbolic regression datasets and results show that the proposed algorithm is able to surpass GSGP, with statistical significance, in terms of learning the training data. In terms of generalization, results are similar to GSGP.
UR - http://www.scopus.com/inward/record.url?scp=84945918595&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23485-4_28
DO - 10.1007/978-3-319-23485-4_28
M3 - Conference contribution
AN - SCOPUS:84945918595
SN - 9783319234847
VL - 9273
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 280
EP - 285
BT - Progress in Artificial Intelligence
PB - Springer-Verlag
Y2 - 8 September 2015 through 11 September 2015
ER -