On the hybridization of geometric semantic GP with gradient-based optimizers

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Pietropolli:2023:GPEM,

• author = "Gloria Pietropolli and Luca Manzoni and Alessia Paoletti and Mauro Castelli",
• title = "On the hybridization of geometric semantic {GP} with gradient-based optimizers",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2023",
• volume = "24",
• number = "2",
• pages = "Article number: 16",
• month = dec,
• note = "Online first",
• keywords = "genetic algorithms, genetic programming, Geometric semantic genetic programming, Stochastic gradient descent, Adam, Evolutionary algorithm",
• ISSN = "1389-2576",
• URL = "https://rdcu.be/dpWsR",
• DOI = "doi:10.1007/s10710-023-09463-1",
• size = "20 pages",
• abstract = "Geometric semantic genetic programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.",
• notes = "Dipartimento di Matematica e Geoscienze, Universita degli Studi di Trieste, Via Alfonso Valerio 12/1, Trieste, 34127, TS, Italy",

}

Genetic Programming entries for Gloria Pietropolli Luca Manzoni Alessia Paoletti Mauro Castelli

Citations