TY - GEN
T1 - A multiple expression alignment framework for genetic programming
AU - Vanneschi, Leonardo
AU - Scott, Kristen
AU - Castelli, Mauro
N1 - Vanneschi, L., Scott, K., & Castelli, M. (2018). A multiple expression alignment framework for genetic programming. In M. Castelli, L. Sekanina, M. Zhang, S. Cagnoni, & P. García-Sánchez (Eds.), Genetic Programming: 21st European Conference, EuroGP 2018, Proceedings, pp. 166-183. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10781 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-77553-1_11
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Alignment in the error space is a recent idea to exploit semantic awareness in genetic programming. In a previous contribution, the concepts of optimally aligned and optimally coplanar individuals were introduced, and it was shown that given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. As a consequence, genetic programming methods, aimed at searching for optimally aligned, or optimally coplanar, individuals were introduced. In this paper, we critically discuss those methods, analyzing their major limitations and we propose new genetic programming systems aimed at overcoming those limitations. The presented experimental results, conducted on four real-life symbolic regression problems, show that the proposed algorithms outperform not only the existing methods based on the concept of alignment in the error space, but also geometric semantic genetic programming and standard genetic programming.
AB - Alignment in the error space is a recent idea to exploit semantic awareness in genetic programming. In a previous contribution, the concepts of optimally aligned and optimally coplanar individuals were introduced, and it was shown that given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. As a consequence, genetic programming methods, aimed at searching for optimally aligned, or optimally coplanar, individuals were introduced. In this paper, we critically discuss those methods, analyzing their major limitations and we propose new genetic programming systems aimed at overcoming those limitations. The presented experimental results, conducted on four real-life symbolic regression problems, show that the proposed algorithms outperform not only the existing methods based on the concept of alignment in the error space, but also geometric semantic genetic programming and standard genetic programming.
UR - http://www.scopus.com/inward/record.url?scp=85044718071&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000787651200011
U2 - 10.1007/978-3-319-77553-1_11
DO - 10.1007/978-3-319-77553-1_11
M3 - Conference contribution
AN - SCOPUS:85044718071
SN - 9783319775524
VL - 10781 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 166
EP - 183
BT - Genetic Programming
A2 - Castelli, Mauro
A2 - Sekanina, Lukas
A2 - Zhang, Mengjie
A2 - Cagnoni, Stefano
A2 - García-Sánchez, Pablo
PB - Springer Verlag
T2 - 21st European Conference on Genetic Programming, EuroGP 2018
Y2 - 4 April 2018 through 6 April 2018
ER -