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

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@Article{castelli:2022:AS,

• author = "Mauro Castelli and Luca Manzoni and Luca Mariot and Giuliamaria Menara and Gloria Pietropolli",
• title = "The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming",
• journal = "Applied Sciences",
• year = "2022",
• volume = "12",
• number = "10",
• pages = "Article No. 4836",
• keywords = "genetic algorithms, genetic programming, evolutionary computation, geometric operators, geometric semantic genetic programming",
• ISSN = "2076-3417",
• URL = "https://www.mdpi.com/2076-3417/12/10/4836",
• DOI = "doi:10.3390/app12104836",
• size = "13 pages",
• abstract = "Among the evolutionary methods, one that is quite prominent is genetic programming. In recent years, a variant called geometric semantic genetic programming (GSGP) was successfully applied to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all its 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.",
• notes = "also known as \cite{app12104836}",

}

Genetic Programming entries for Mauro Castelli Luca Manzoni Luca Mariot Giuliamaria Menara Gloria Pietropolli

Citations