The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming

Autor:	Castelli, Mauro, Manzoni, Luca, Mariot, Luca, Menara, Giuliamaria, Pietropolli, Gloria
Rok vydání:	2022
Předmět:	Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	Among the evolutionary methods, one that is quite prominent is Genetic Programming, and, in recent years, a variant called Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all the evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations. We show that a limited ability to use "old" generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP. Comment: 19 pages, 4 figures, 5 tables. Submitted to Applied Sciences
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.02598 Zobrazit plný text záznamu View this record from Arxiv