A study of dynamic populations in geometric semantic genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7650

@Article{Farinati:2023:INS,

• author = "Davide Farinati and Illya Bakurov and Leonardo Vanneschi",
• title = "A study of dynamic populations in geometric semantic genetic programming",
• journal = "Information Sciences",
• year = "2023",
• volume = "648",
• pages = "119513",
• month = nov,
• keywords = "genetic algorithms, genetic programming, Dynamic populations, Geometric semantic genetic programming, Semantic neighbourhood",
• ISSN = "0020-0255",
• URL = "https://www.sciencedirect.com/science/article/pii/S0020025523010988",
• DOI = "doi:10.1016/j.ins.2023.119513",
• abstract = "Allowing the population size to variate during the evolution can bring advantages to evolutionary algorithms (EAs), retaining computational effort during the evolution process. Dynamic populations use computational resources wisely in several types of EAs, including genetic programming. However, so far, a thorough study on the use of dynamic populations in Geometric Semantic Genetic Programming (GSGP) is missing. Still, GSGP is a resource-greedy algorithm, and the use of dynamic populations seems appropriate. we adapt algorithms to GSGP to manage dynamic populations that were successful for other types of EAs and introduces two novel algorithms. The novel algorithms exploit the concept of semantic neighbourhood. These methods are assessed and compared through a set of eight regression problems. The results indicate that the algorithms outperform standard GSGP, confirming the suitability of dynamic populations for GSGP. the novel algorithms that use semantic neighbourhood to manage variation in population size are particularly effective in generating robust models even for the most difficult of the studied test problems.",
• notes = "also known as \cite{FARINATI2023119513}",

}

Genetic Programming entries for Davide Farinati Illya Bakurov Leonardo Vanneschi

Citations