A study of dynamic populations in geometric semantic genetic programming
Created by W.Langdon from
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- @Article{Farinati:2023:INS,
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author = "Davide Farinati and Illya Bakurov and
Leonardo Vanneschi",
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title = "A study of dynamic populations in geometric semantic
genetic programming",
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journal = "Information Sciences",
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year = "2023",
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volume = "648",
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pages = "119513",
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month = nov,
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keywords = "genetic algorithms, genetic programming, Dynamic
populations, Geometric semantic genetic programming,
Semantic neighbourhood",
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ISSN = "0020-0255",
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URL = "https://www.sciencedirect.com/science/article/pii/S0020025523010988",
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DOI = "doi:10.1016/j.ins.2023.119513",
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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.",
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notes = "also known as \cite{FARINATI2023119513}",
- }
Genetic Programming entries for
Davide Farinati
Illya Bakurov
Leonardo Vanneschi
Citations