- author = "Gloria Pietropolli and Luca Manzoni and Alessia Paoletti and Mauro Castelli",
- title = "Combining Geometric Semantic {GP} with Gradient-descent Optimization",
- booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming",
- year = "2022",
- editor = "Eric Medvet and Gisele Pappa and Bing Xue",
- series = "LNCS",
- volume = "13223",
- publisher = "Springer Verlag",
- address = "Madrid, Spain",
- pages = "17--32",
- month = "20-22 " # apr,
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming, Geometric semantic genetic programming",
- isbn13 = "978-3-031-02055-1",
-
DOI = "
doi:10.1007/978-3-031-02056-8_2",
-
abstract = "Geometric semantic genetic programming (GSGP) is a
well-known variant of genetic programming (GP) where
recombination and mutation operators have a clear
semantic effect. Both kind of operators have randomly
selected parameters that are not optimized by the
search process. In this paper we combine GSGP with a
well-known gradient-based optimizer, Adam, in order to
leverage the ability of GP to operate structural
changes of the individuals with the ability of
gradient-based methods to optimize the parameters of a
given structure.
Two methods, named HYB-GSGP and HeH-GSGP, are defined and compared with GSGP on a large set of regression problems, showing that the use of Adam can improve the performance on the test set. The idea of merging evolutionary computation and gradient-based optimization is a promising way of combining two methods with very different and complementary strengths.",
- notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022",
